A Prospective Study on Clinical Characteristics, Antibiotic Susceptibility Patterns, and Factors Predicting Mortality Associated with E. Meningoseptica Infections in Critically Ill Patients Admitted to A Tertiary Care Hospital in Eastern India

Background: Elizabethkingiameningoseptica is an emerging multidrug-resistant pathogen associated with high mortality in critically ill patients. Data on clinical characteristics, antibiotic susceptibility, and outcomes in the Indian ICU setting is limited. Methods: This prospective study was conducted in the medical ICU of a IMS & SUM tertiary care hospital in Eastern India from May 2019 to August 2022. Critically ill patients with E. meningoseptica isolated from sterile specimens were included. Demographic details, comorbidities, microbiological data, treatment details, and outcome were noted. Results: Of 3700 ICU admissions, 35 patients developed E. meningoseptica infection. Mean age was 60.2 years and 63% were males. Majority had pneumonia (71%) or urosepsis (14%). Key comorbidities were hypertension (49%), diabetes (43%), chronic kidney disease (40%), and chronic dialysis (23%). All isolates were sensitive to Minocycline while 43% were sensitive to Levofloxacin and ciprofloxacin each. Empiric minocycline-levofloxacin combination was used in 49% patients. Crude mortality was 54%. Mortality was significantly associated with higher severity scores, coronary artery disease, acute/chronic kidney injury, respiratory co-infection, and prolonged ICU stay. Mortality was found to be lower in case of Minocycline and Levofloxacin combination therapy (35%) compared to Minocycline and Levofloxacin monotherapy (75%). Conclusion: E. meningoseptica caused high mortality in critically ill patients despite treatment. There is increasing incidence of resistance to fluroquinolones among the E. meingoseptica isolates found in the ICU and all were susceptible to Minocycline. Combination regimens with Minocycline may be studied in future randomized trials to study the efficacy in treating E. meningoseptica infections.  Infection control and antimicrobial stewardship are keys to prevent further resistance

An Audit of primary ovarian pregnancy in a tertiary care hospital

Background: Ovarian pregnancy is uncommon form of ectopic pregnancy with increasing trend in its incidence. Various risk factors have been implicated like intrauterine contraceptive devices, fertility treatments, pelvic inflammatory disease (PID), and endometriosis.Methods: This is a retrospective observational study done over a period of 10 years from January 2008 to December 2017.All the patients, who were diagnosed as primary ovarian pregnancy based on Spielberg’s criteria, were included in the study.Results: There were 6 cases (6.3%) of primary ovarian pregnancy out of the 95 cases of ectopic pregnancies.  The age of the patients ranged from 28 years to 43 years with a mean age of 31.8 years. Amenorrhea and acute pain abdomen was present in all the patients. Laparotomy was done in all the cases. Excision of the sac with partial ovariectomy was done in 2 cases (33.3%).Oophorectomy with salpingectomy was done in the rest of the 4 cases (66.7%). Histopathological confirmation was done in all the cases.Conclusions: Ovarian pregnancy is uncommon form of ectopic pregnancy with increasing trend in its incidence. Management of choice is laparoscopy. Laparotomy is done if there are signs of rupture

Status of Library Automation among Government Autonomous College Libraries in Odisha: Challenges and Opportunities

The study examines the status of library automation in Autonomous College libraries of Odisha Government. Also discusses the status of library automation and its challenges with a comprehensive overview of libraries embracing digital transformation. The findings of the study shows that institutional priorities, funding, and administrative support affect automation levels. Targeted investments in software, training, and network infrastructure can boost growth despite limited resources and technical expertise. The study recommends improving library automation to improve accessibility, efficiency, and resource-sharing across Odisha's academic network

Analysis of the prevalence, etiology, and risk factors of stillbirth from a teaching institute of North Eastern India- a retrospective study

Background: Stillbirth rate is considered a health index. The worldwide stillbirth rate is 18.4/1000 total birth. This study was aimed to evaluate the prevalence and risk factors of intrauterine fetal death in pregnant women in one of the teaching centers in Northeastern India.Methods: This was a retrospective study. All cases of intrauterine fetal death admitted in the department of obstetrics gynecology of our institute were included over two and half years. Information was gathered from the medical records of the patients and data were analyzed.Results: During two and half year’s period, the total number of deliveries was 2696 and the total numbers of stillbirths were 96, hence the stillbirth rate was 35.6/1000. 93 (96.87%) were antenatal stillbirths and 3 (3.12%) were intrapartum stillbirths. 82 (85.41%) women were unbooked. 85 (90.4%) belonged to low socioeconomic status. 67 (69.79%) were preterm. Maximum 39 (40.62%) belonged to 28-35 weeks of gestational age. The most common cause of Intrauterine death (IUD) was antepartum hemorrhage (17.7%). 14 (14.5%) were abruption and 3 were placenta previa. The second most common cause (14.5%) was the hypertensive disorder of pregnancy.Conclusions: The stillbirth rate in our institute is higher than the national average. The most common causes of IUD were antepartum hemorrhage, preeclampsia, prematurity, and malpresentation which can be diagnosed and managed by increasing uptake of antenatal care which will lead to timely identification and proper management of maternal and fetal complications eventually reducing the preventable stillbirths

Comparison between oral melatonin and 24% sucrose for pain management during retinopathy of prematurity screening: a randomized controlled trial

Background. Preterm neonates perceive multiple painful procedures during Neonatal Intensive Care Unit (NICU) stay, having long term neurobehavioral effects. This study aims to compare the analgesic efficacy of oral melatonin with 24% sucrose in neonates during retinopathy of prematurity (ROP) screening. Methods. A prospective, non-blinded, randomized controlled trial was conducted in a tertiary care NICU. All preterm neonates with gestational age (GA) < 34 weeks or birth weight (BW) < 2000 grams eligible for ROP screening were randomized into oral melatonin (4 mg/kg) and oral 24% sucrose (0.5 ml) groups. Both groups received standard non-pharmacological measures and topical proparacaine. The intensity of pain was measured by Premature Infant Pain Profile (PIPP) score during the procedure, at 1st and 5th minutes following the procedure and compared between the two groups by Mann-Whitney U test with p value < 0.05 considered as significant. Results. A total of 60 preterm neonates were randomized with 30 neonates in the melatonin (median [interquartile range] GA: 30.86 [3.78] weeks, BW: 1160 [430] grams) and 30 neonates in the 24% sucrose (median [IQR] GA: 29.29 [4.68] weeks, BW: 1070 [315] grams) group. The median PIPP score during the procedure in the melatonin and sucrose groups were 17 and 16, respectively (p=0.64). The median (Q1-Q3) PIPP score at the 1st minute was significantly lower among the melatonin group (7 [5.25-10]) vs 24% sucrose group (9.5 [7.25-11]) (p=0.02); and at the 5th minute, the median (Q1-Q3) PIPP scores in the melatonin group (5 [4-6]) was comparable to the 24% sucrose group (5.5 [3.25-7]) (p= 0.52). Conclusions. Oral melatonin is not inferior to oral 24% sucrose for pain management during ROP screening

Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Background: Understanding the health consequences associated with exposure to risk factors is necessary to inform public health policy and practice. To systematically quantify the contributions of risk factor exposures to specific health outcomes, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 aims to provide comprehensive estimates of exposure levels, relative health risks, and attributable burden of disease for 88 risk factors in 204 countries and territories and 811 subnational locations, from 1990 to 2021. Methods: The GBD 2021 risk factor analysis used data from 54 561 total distinct sources to produce epidemiological estimates for 88 risk factors and their associated health outcomes for a total of 631 risk–outcome pairs. Pairs were included on the basis of data-driven determination of a risk–outcome association. Age-sex-location-year-specific estimates were generated at global, regional, and national levels. Our approach followed the comparative risk assessment framework predicated on a causal web of hierarchically organised, potentially combinative, modifiable risks. Relative risks (RRs) of a given outcome occurring as a function of risk factor exposure were estimated separately for each risk–outcome pair, and summary exposure values (SEVs), representing risk-weighted exposure prevalence, and theoretical minimum risk exposure levels (TMRELs) were estimated for each risk factor. These estimates were used to calculate the population attributable fraction (PAF; ie, the proportional change in health risk that would occur if exposure to a risk factor were reduced to the TMREL). The product of PAFs and disease burden associated with a given outcome, measured in disability-adjusted life-years (DALYs), yielded measures of attributable burden (ie, the proportion of total disease burden attributable to a particular risk factor or combination of risk factors). Adjustments for mediation were applied to account for relationships involving risk factors that act indirectly on outcomes via intermediate risks. Attributable burden estimates were stratified by Socio-demographic Index (SDI) quintile and presented as counts, age-standardised rates, and rankings. To complement estimates of RR and attributable burden, newly developed burden of proof risk function (BPRF) methods were applied to yield supplementary, conservative interpretations of risk–outcome associations based on the consistency of underlying evidence, accounting for unexplained heterogeneity between input data from different studies. Estimates reported represent the mean value across 500 draws from the estimate's distribution, with 95% uncertainty intervals (UIs) calculated as the 2·5th and 97·5th percentile values across the draws. Findings: Among the specific risk factors analysed for this study, particulate matter air pollution was the leading contributor to the global disease burden in 2021, contributing 8·0% (95% UI 6·7–9·4) of total DALYs, followed by high systolic blood pressure (SBP; 7·8% [6·4–9·2]), smoking (5·7% [4·7–6·8]), low birthweight and short gestation (5·6% [4·8–6·3]), and high fasting plasma glucose (FPG; 5·4% [4·8–6·0]). For younger demographics (ie, those aged 0–4 years and 5–14 years), risks such as low birthweight and short gestation and unsafe water, sanitation, and handwashing (WaSH) were among the leading risk factors, while for older age groups, metabolic risks such as high SBP, high body-mass index (BMI), high FPG, and high LDL cholesterol had a greater impact. From 2000 to 2021, there was an observable shift in global health challenges, marked by a decline in the number of all-age DALYs broadly attributable to behavioural risks (decrease of 20·7% [13·9–27·7]) and environmental and occupational risks (decrease of 22·0% [15·5–28·8]), coupled with a 49·4% (42·3–56·9) increase in DALYs attributable to metabolic risks, all reflecting ageing populations and changing lifestyles on a global scale. Age-standardised global DALY rates attributable to high BMI and high FPG rose considerably (15·7% [9·9–21·7] for high BMI and 7·9% [3·3–12·9] for high FPG) over this period, with exposure to these risks increasing annually at rates of 1·8% (1·6–1·9) for high BMI and 1·3% (1·1–1·5) for high FPG. By contrast, the global risk-attributable burden and exposure to many other risk factors declined, notably for risks such as child growth failure and unsafe water source, with age-standardised attributable DALYs decreasing by 71·5% (64·4–78·8) for child growth failure and 66·3% (60·2–72·0) for unsafe water source. We separated risk factors into three groups according to trajectory over time: those with a decreasing attributable burden, due largely to declining risk exposure (eg, diet high in trans-fat and household air pollution) but also to proportionally smaller child and youth populations (eg, child and maternal malnutrition); those for which the burden increased moderately in spite of declining risk exposure, due largely to population ageing (eg, smoking); and those for which the burden increased considerably due to both increasing risk exposure and population ageing (eg, ambient particulate matter air pollution, high BMI, high FPG, and high SBP). Interpretation: Substantial progress has been made in reducing the global disease burden attributable to a range of risk factors, particularly those related to maternal and child health, WaSH, and household air pollution. Maintaining efforts to minimise the impact of these risk factors, especially in low SDI locations, is necessary to sustain progress. Successes in moderating the smoking-related burden by reducing risk exposure highlight the need to advance policies that reduce exposure to other leading risk factors such as ambient particulate matter air pollution and high SBP. Troubling increases in high FPG, high BMI, and other risk factors related to obesity and metabolic syndrome indicate an urgent need to identify and implement interventions

Constraints on the Cosmic Expansion History from GWTC-3

This material is based upon work supported by NSFʼs LIGO Laboratory, which is a major facility fully funded by the National Science Foundation. The authors also gratefully acknowledge the support of the Science and Technology Facilities Council (STFC) of the United Kingdom, the Max-Planck-Society (MPS), and the State of Niedersachsen/Germany for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the Australian Research Council. The authors gratefully acknowledge the Italian Istituto Nazionale di Fisica Nucleare (INFN), the French Centre National de la Recherche Scientifique (CNRS), and the Netherlands Organization for Scientific Research (NWO), for the construction and operation of the Virgo detector and the creation and support of the EGO consortium. The authors also gratefully acknowledge research support from these agencies as well as by the Council of Scientific and Industrial Research of India, the Department of Science and Technology, India, the Science & Engineering Research Board (SERB), India, the Ministry of Human Resource Development, India, the Spanish Agencia Estatal de Investigación (AEI), the Spanish Ministerio de Ciencia e Innovación and Ministerio de Universidades, the Conselleria de Fons Europeus, Universitat i Cultura and the Direcció General de Política Universitaria i Recerca del Govern de les Illes Balears, the Conselleria d’Innovació Universitats, Ciència i Societat Digital de la Generalitat Valenciana and the CERCA Programme Generalitat de Catalunya, Spain, the National Science Centre of Poland and the European Union–European Regional Development Fund, Foundation for Polish Science (FNP), the Swiss National Science Foundation (SNSF), the Russian Foundation for Basic Research, the Russian Science Foundation, the European Commission, the European Social Funds (ESF), the European Regional Develop- ment Funds (ERDF), the Royal Society, the Scottish Funding Council, the Scottish Universities Physics Alliance, the Hungarian Scientific Research Fund (OTKA), the French Lyon Institute of Origins (LIO), the Belgian Fonds de la Recherche Scientifique (FRS-FNRS), Actions de Recherche Concertées (ARC) and Fonds Wetenschappelijk Onderzoek–Vlaanderen (FWO), Bel- gium, the Paris Île-de-France Region, the National Research, Development and Innovation Office Hungary (NKFIH), the National Research Foundation of Korea, the Natural Science and Engineering Research Council Canada, Canadian Foundation for Innovation (CFI), the Brazilian Ministry of Science, Technology, and Innovations, the International Center for Theoretical Physics South American Institute for Fundamental Research (ICTP- SAIFR), the Research Grants Council of Hong Kong, the National Natural Science Foundation of China (NSFC), the Leverhulme Trust, the Research Corporation, the Ministry of Science and Technology (MOST), Taiwan, the United States Department of Energy, and the Kavli Foundation. The authors gratefully acknowledge the support of the NSF, STFC, INFN, and CNRS for provision of computational resources. This work was supported by MEXT, JSPS Leading-edge Research Infrastructure Program, JSPS Grant-in-Aid for Specially Promoted Research 26000005, JSPS Grant-in-Aid for Scientific Research on Innovative Areas 2905: JP17H06358, JP17H06361, and JP17H06364, JSPS Core-to- Core Program A. Advanced Research Networks, JSPS Grant- in-Aid for Scientific Research (S) 17H06133 and 20H05639, JSPS Grant-in-Aid for Transformative Research Areas (A) 20A203: JP20H05854, the joint research program of the Institute for Cosmic Ray Research, University of Tokyo, National Research Foundation (NRF) and Computing Infra- structure Project of KISTI-GSDC in Korea, Academia Sinica (AS), AS Grid Center (ASGC), and the Ministry of Science and Technology (MoST) in Taiwan under grants including AS- CDA-105-M06, Advanced Technology Center (ATC) of NAOJ, Mechanical Engineering Center of KEK. We would like to thank all of the essential workers who put their health at risk during the COVID-19 pandemic, without whom we would not have been able to complete this work.Peer reviewe

Open Data from the Third Observing Run of LIGO, Virgo, KAGRA, and GEO

Calibration of the LIGO strain data was performed with a GstLAL-based calibration software pipeline (Viets et al. 2018). Calibration of the Virgo strain data was performed with C-based software (Acernese et al. 2022b). Data quality products and event-validation results were computed using the DMT (https://labcit.ligo.caltech.edu/~jzweizig/DMT-Project. html), DQR (https://docs.ligo.org/detchar/data-quality-report/), DQSEGDB (Fisher et al. 2021), gwdetchar (Macloed et al. 2021a), hveto (Smith et al. 2011), iDQ (Essick et al. 2020), and Omicron (Robinet et al. 2020) software packages and contribut- ing software tools. Analyses relied upon the LALSuite software library (LIGO Scientific Collaboration 2018). PESummary was used to postprocess and collate parameter estimation results (Hoy & Raymond 2021). For an exhaustive list of the software used for searching the GW signals and characterizing their source, see Abbott et al. (2021c). Plots were prepared with Matplotlib (Hunter 2007), seaborn (Waskom 2021), GWSumm (Macleod et al. 2021b), and GWpy (Macleod et al. 2021c). NumPy (Harris et al. 2020) and SciPy (Virtanen et al. 2020) were used in the preparation of the manuscript. This material is based upon work supported by NSF’s LIGO Laboratory which is a major facility fully funded by the National Science Foundation. The authors also gratefully acknowledge the support of the Science and Technology Facilities Council (STFC) of the United Kingdom, the Max- Planck-Society (MPS), and the State of Niedersachsen/ Germany for support of the construction of Advanced LIGO and construction and operation of the GEO 600 detector. Additional support for Advanced LIGO was provided by the Australian Research Council. The authors gratefully acknowl- edge the Italian Istituto Nazionale di Fisica Nucleare (INFN), the French Centre National de la Recherche Scientifique (CNRS), and the Netherlands Organization for Scientific Research (NWO) for the construction and operation of the Virgo detector and the creation and support of the EGO consortium. The authors also gratefully acknowledge research support from these agencies as well as by the Council of Scientific and Industrial Research of India, the Department of Science and Technology, India, the Science & Engineering Research Board (SERB), India, the Ministry of Human Resource Development, India, the Spanish Agencia Estatal de Investigación (AEI), the Spanish Ministerio de Ciencia e Innovación and Ministerio de Universidades, the Conselleria de Fons Europeus, Universitat i Cultura and the Direcció General de Política Universitaria i Recerca del Govern de les Illes Balears, the Conselleria d'Innovació, Universitats, Ciència i Societat Digital de la Generalitat Valenciana and the CERCA Programme Generalitat de Catalunya, Spain, the National Science Centre of Poland and the European Union – European Regional Development Fund; Foundation for Polish Science (FNP), the Swiss National Science Foundation (SNSF), the Russian Foundation for Basic Research, the Russian Science Foundation, the European Commission, the European Social Funds (ESF), the European Regional Development Funds (ERDF), the Royal Society, the Scottish Funding Council, the Scottish Universities Physics Alliance, the Hungarian Scientific Research Fund (OTKA), the French Lyon Institute of Origins (LIO), the Belgian Fonds de la Recherche Scientifique (FRS- FNRS), Actions de Recherche Concertées (ARC) and Fonds Wetenschappelijk Onderzoek – Vlaanderen (FWO), Belgium, the Paris Île-de-France Region, the National Research, Development and Innovation Office Hungary (NKFIH), the National Research Foundation of Korea, the Natural Science and Engineering Research Council Canada, Canadian Founda- tion for Innovation (CFI), the Brazilian Ministry of Science, Technology, and Innovations, the International Center for Theoretical Physics South American Institute for Fundamental Research (ICTP-SAIFR), the Research Grants Council of Hong Kong, the National Natural Science Foundation of China (NSFC), the Leverhulme Trust, the Research Corporation, the Ministry of Science and Technology (MOST), Taiwan, the United States Department of Energy, and the Kavli Foundation. The authors gratefully acknowledge the support of the NSF, STFC, INFN, and CNRS for provision of computational resources. This work was supported by MEXT, JSPS Leading-edge Research Infrastructure Program, JSPS Grant-in-Aid for Specially Promoted Research 26000005, JSPS Grant-in-Aid for Scientific Research on Innovative Areas 2905: JP17H06358, JP17H06361 and JP17H06364, JSPS Core-to- Core Program A, Advanced Research Networks, JSPS Grant- in-Aid for Scientific Research (S) 17H06133 and 20H05639, JSPS Grant-in-Aid for Transformative Research Areas (A) 20A203: JP20H05854, the joint research program of the Institute for Cosmic Ray Research, University of Tokyo, National Research Foundation (NRF), Computing Infrastruc- ture Project of Global Science experimental Data hub Center (GSDC) at KISTI, Korea Astronomy and Space Science Institute (KASI), and Ministry of Science and ICT (MSIT) in Korea, Academia Sinica (AS), AS Grid Center (ASGC) and the National Science and Technology Council (NSTC) in Taiwan under grants including the Rising Star Program and Science Vanguard Research Program, Advanced Technology Center (ATC) of NAOJ, and Mechanical Engineering Center of KEK.Peer reviewe

Ultralight vector dark matter search using data from the KAGRA O3GK run

Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for U(1)B−L gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the U(1)B−L gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM