Tag: nevin manimala

BMC Geriatr. 2022 Aug 22;22(1):691. doi: 10.1186/s12877-022-03389-6.

ABSTRACT

As the population ages, the number of people living with Alzheimer’s disease is expected to grow; consequently, nursing students are expected to care for more people with Alzheimer’s disease in their future careers. Exploring nursing students’ level of knowledge and attitudes is essential here to fill any knowledge gap and enhance attitudes. For this reason, the current study aimed to measure the knowledge of and attitudes toward people living with Alzheimer’s disease among undergraduate Jordanian nursing students. A descriptive cross-sectional design was utilized. Data were collected through an online questionnaire consisting of the Alzheimer’s Disease Knowledge Scale (ADKS) and Dementia Attitudes Scale (DAS). A third part contained questions about previous formal education about Alzheimer’s disease, reading Alzheimer’s research, and the need for formal education about Alzheimer’s disease. The study targeted all undergraduate Jordanian nursing students. A total of 275 students agreed to participate and completed the questionnaire. Jordanian nursing students had low knowledge regarding people living with Alzheimer’s disease, with a mean ADKS score of 18.3 out of 30; however, their attitudes were positive, with a mean DAS score of 91 out of 140. There was no statistical difference in attitude or knowledge between different academic levels. The majority of students (90.5%) expressed their desire to have a formal education regarding Alzheimer’s disease. Knowledge regarding people with Alzheimer’s disease could be improved through training and education. Positive attitudes reported by students could augment the learning process.

PMID:35996080 | DOI:10.1186/s12877-022-03389-6

BMC Anesthesiol. 2022 Aug 22;22(1):267. doi: 10.1186/s12871-022-01804-4.

ABSTRACT

BACKGROUND: Postoperative delirium (POD) is a critical complication in patients accepting colon carcinoma surgery. Neostigmine, as a cholinesterase inhibitor, can enhance the transmission of cholinergic transmitters in synaptic space, and play an important role in maintaining the normal level of cognition, attention and consciousness. The objective of this study was to investigate the effect of neostigmine on POD and clinical prognosis.

METHODS: A randomized, double-blind controlled trial was implemented in Qingdao Municipal Hospital Affiliated to Qingdao University. A total of 454 patients aged 40 to 90 years old accepted colon carcinoma surgery were enrolled between June 7, 2020, and June 7, 2021, with final follow-up on December 8, 2021. Patients were randomly assigned to two groups: the neostigmine group (group N) and the placebo group (group P), the patients in group N were injected with 0.04 mg/kg neostigmine and 0.02 mg/kg atropine intravenously. The primary endpoint was the incidence of POD, researchers evaluated the occurrence of POD by the Confusion Assessment Method (CAM) twice daily (at 10 a.m. and 2 p.m.) during the first 7 postoperative days, POD severity was assessed by the Memorial Delirium Assessment Scale (MDAS). The secondary endpoints were the extubating time, postanesthesia care unit (PACU) time, the incidence of various postoperative complications, length of hospital stays, and 6 months postoperative mortality.

RESULTS: The incidence of POD was 20.20% (81/401), including 19.39% (38/196) in group N and 20.98% (43/205) in group P. There was no significant statistical significance in the incidence of POD between group N and group P (P > 0.05); Compared to group P, the extubating time and PACU time in group N were significantly reduced (P < 0.001), the incidence of postoperative pulmonary complications (POPCs) decreased significantly in group N (P < 0.05), while no significant differences were observed in postoperative hospital stay and mortality in 6 months between the two groups (P > 0.05).

CONCLUSION: For patients accepted colon carcinoma surgery, neostigmine did not significantly reduce the incidence of POD, postoperative mortality and postoperative hospital stay, while it indeed reduced the extubating time, PACU time and the incidence of POPCs.

TRIAL REGISTRATION: The randomized, double-blind, controlled trial was registered retrospectively at www.chictr.org.cn on 07/06/2020 (ChiCTR2000033639).

PMID:35996073 | DOI:10.1186/s12871-022-01804-4

Ann Intern Med. 2022 Aug 23. doi: 10.7326/M22-0988. Online ahead of print.

ABSTRACT

BACKGROUND: The BNT162b2 (Pfizer-BioNTech) vaccine has been shown to be safe with regard to risk for severe cardiovascular events (such as myocardial infarction [MI], pulmonary embolism [PE], and stroke) in persons aged 75 years or older. Less is known about the safety of other COVID-19 vaccines or outcomes in younger populations.

OBJECTIVE: To assess short-term risk for severe cardiovascular events (excluding myocarditis and pericarditis) after COVID-19 vaccination in France’s 46.5 million adults younger than 75 years.

DESIGN: Self-controlled case series method adapted to event-dependent exposure and high event-related mortality.

SETTING: France, 27 December 2020 to 20 July 2021.

PATIENTS: All adults younger than 75 years hospitalized for PE, acute MI, hemorrhagic stroke, or ischemic stroke (n = 73 325 total events).

MEASUREMENTS: Linkage between the French National Health Data System and COVID-19 vaccine databases enabled identification of hospitalizations for cardiovascular events (MI, PE, or stroke) and receipt of a first or second dose of the Pfizer-BioNTech, mRNA-1273 (Moderna), Ad26.COV2.S (Janssen), or ChAdOx1 nCoV-19 (Oxford-AstraZeneca) vaccine. The relative incidence (RI) of each cardiovascular event was estimated in the 3 weeks after vaccination compared with other periods, with adjustment for temporality (7-day periods).

RESULTS: No association was found between the Pfizer-BioNTech or Moderna vaccine and severe cardiovascular events. The first dose of the Oxford-AstraZeneca vaccine was associated with acute MI and PE in the second week after vaccination (RI, 1.29 [95% CI, 1.11 to 1.51] and 1.41 [CI, 1.13 to 1.75], respectively). An association with MI in the second week after a single dose of the Janssen vaccine could not be ruled out (RI, 1.75 [CI, 1.16 to 2.62]).

LIMITATIONS: It was not possible to ascertain the relative timing of injection and cardiovascular events on the day of vaccination. Outpatient deaths related to cardiovascular events were not included.

CONCLUSION: In persons aged 18 to 74 years, adenoviral-based vaccines may be associated with increased incidence of MI and PE. No association between mRNA-based vaccines and the cardiovascular events studied was observed.

PRIMARY FUNDING SOURCE: None.

PMID:35994748 | DOI:10.7326/M22-0988

J Trop Pediatr. 2022 Aug 4;68(5):fmac072. doi: 10.1093/tropej/fmac072.

ABSTRACT

AIM: In this study, we aimed to evaluate serum vitamin D and zinc levels in children diagnosed with coronavirus disease 2019 (COVID-19).

MATERIALS AND METHODS: In this study, 88 children with COVID-19 disease and 88 healthy children aged 1-18 years were enrolled between 01 July 2021 and 30 October 2021 in the Pediatrics Clinic of Tekirdağ Çorlu State Hospital. Serum vitamin D and zinc levels have been measured and NCSS (Number Cruncher Statistical System) program has been utilized for statistical analysis.

RESULTS: We included 88 COVID-19 positive pediatric patients [50% (n = 44) female] and 88 healthy children [48.86% (n = 43) female] in this study. The mean serum vitamin D levels of COVID-19 positive patients were statistically significantly lower than the control group (p = 0.0001). The zinc mean values of the study group were found to be statistically significantly lower than the control group (p = 0.0001). There was a statistically significant correlation between serum vitamin D and zinc values in all patient groups (r = 0.245, p = 0.001).

CONCLUSION: As a result, zinc and vitamin D levels were observed lower in COVID-19 patients than in healthy individuals. Since there is no defined treatment protocol for COVID-19 infection on children yet, zinc and vitamin D supplementation can be used as a supportive treatment in COVID-19 infection.

PMID:35994727 | DOI:10.1093/tropej/fmac072

J Phys Chem A. 2022 Aug 22. doi: 10.1021/acs.jpca.2c04265. Online ahead of print.

ABSTRACT

We have recorded the resonance-enhanced multiphoton ionization spectrum for NO (A) products from photodissociation of the N₂-NO complex. We made measurements at excitation energies ranging from 28 to 758 cm^-1 above the threshold to produce NO (A) + N₂ (X) products, and the resulting spectra reveal the NO (A) rotational states formed during dissociation, allowing us to determine the rotational state distribution. At the lowest available energies, 28 and 50 cm^-1 above threshold, we observed contributions from NO (A) rotational states that exceed the available energy and must originate from excitation due to hotbands of the complex. At all higher energies, we did not observe any energetically disallowed NO (A) rotational states, and for all available energies above 259 cm^-1 the observed rotational transitions do not extend to the maximum allowed by energy conservation. Furthermore, the observed distributions were typically biased toward low rotational states, in contrast with expectations from vibrational predissociation. From the rotational state distributions, we determined the average fraction of energy partitioned into NO (A) rotation, f_{NO rot, ave}, to be 0.088 at the highest available energy, and this fraction increased as the available energy decreased. By combining the average NO (A) rotational energy along with the average center-of-mass translational energy from our previous work, we determined the average rotational energy for the undetected N₂ (X) photoproduct. The results showed that the N₂ fragment has a higher average rotational energy relative to the NO fragment. Finally, we found that the NO (A) rotational state distribution was colder than expected for a statistical dissociation.

PMID:35994689 | DOI:10.1021/acs.jpca.2c04265

Proc Natl Acad Sci U S A. 2022 Aug 30;119(35):e2119513119. doi: 10.1073/pnas.2119513119. Epub 2022 Aug 22.

ABSTRACT

In a striking result, Louca and Pennell [S. Louca, M. W. Pennell, Nature 580, 502-505 (2020)] recently proved that a large class of phylogenetic birth-death models is statistically unidentifiable from lineage-through-time (LTT) data: Any pair of sufficiently smooth birth and death rate functions is “congruent” to an infinite collection of other rate functions, all of which have the same likelihood for any LTT vector of any dimension. As Louca and Pennell argue, this fact has distressing implications for the thousands of studies that have utilized birth-death models to study evolution. In this paper, we qualify their finding by proving that an alternative and widely used class of birth-death models is indeed identifiable. Specifically, we show that piecewise constant birth-death models can, in principle, be consistently estimated and distinguished from one another, given a sufficiently large extant timetree and some knowledge of the present-day population. Subject to mild regularity conditions, we further show that any unidentifiable birth-death model class can be arbitrarily closely approximated by a class of identifiable models. The sampling requirements needed for our results to hold are explicit and are expected to be satisfied in many contexts such as the phylodynamic analysis of a global pandemic.

PMID:35994663 | DOI:10.1073/pnas.2119513119

Proc Natl Acad Sci U S A. 2022 Aug 30;119(35):e2121338119. doi: 10.1073/pnas.2121338119. Epub 2022 Aug 22.

ABSTRACT

Precisely how humans process relational patterns of information in knowledge, language, music, and society is not well understood. Prior work in the field of statistical learning has demonstrated that humans process such information by building internal models of the underlying network structure. However, these mental maps are often inaccurate due to limitations in human information processing. The existence of such limitations raises clear questions: Given a target network that one wishes for a human to learn, what network should one present to the human? Should one simply present the target network as-is, or should one emphasize certain parts of the network to proactively mitigate expected errors in learning? To investigate these questions, we study the optimization of network learnability in a computational model of human learning. Evaluating an array of synthetic and real-world networks, we find that learnability is enhanced by reinforcing connections within modules or clusters. In contrast, when networks contain significant core-periphery structure, we find that learnability is best optimized by reinforcing peripheral edges between low-degree nodes. Overall, our findings suggest that the accuracy of human network learning can be systematically enhanced by targeted emphasis and de-emphasis of prescribed sectors of information.

PMID:35994661 | DOI:10.1073/pnas.2121338119

Proc Natl Acad Sci U S A. 2022 Aug 30;119(35):e2203822119. doi: 10.1073/pnas.2203822119. Epub 2022 Aug 22.

ABSTRACT

We propose a method for forecasting global human migration flows. A Bayesian hierarchical model is used to make probabilistic projections of the 39,800 bilateral migration flows among the 200 most populous countries. We generate out-of-sample forecasts for all bilateral flows for the 2015 to 2020 period, using models fitted to bilateral migration flows for five 5-y periods from 1990 to 1995 through 2010 to 2015. We find that the model produces well-calibrated out-of-sample forecasts of bilateral flows, as well as total country-level inflows, outflows, and net flows. The mean absolute error decreased by 61% using our method, compared to a leading model of international migration. Out-of-sample analysis indicated that simple methods for forecasting migration flows offered accurate projections of bilateral migration flows in the near term. Our method matched or improved on the out-of-sample performance using these simple deterministic alternatives, while also accurately assessing uncertainty. We integrate the migration flow forecasting model into a fully probabilistic population projection model to generate bilateral migration flow forecasts by age and sex for all flows from 2020 to 2025 through 2040 to 2045.

PMID:35994637 | DOI:10.1073/pnas.2203822119

Retina. 2022 Sep 1;42(9):1673-1682. doi: 10.1097/IAE.0000000000003557.

ABSTRACT

BACKGROUND/PURPOSE: To apply an automated deep learning automated fluid algorithm on data from real-world management of patients with neovascular age-related macular degeneration for quantification of intraretinal/subretinal fluid volumes in optical coherence tomography images.

METHODS: Data from the Vienna Imaging Biomarker Eye Study (VIBES, 2007-2018) were analyzed. Databases were filtered for treatment-naive neovascular age-related macular degeneration with a baseline optical coherence tomography and at least one follow-up and 1,127 eyes included. Visual acuity and optical coherence tomography at baseline, Months 1 to 3/Years 1 to 5, age, sex, and treatment number were included. Artificial intelligence and certified manual grading were compared in a subanalysis of 20%. Main outcome measures were fluid volumes.

RESULTS: Intraretinal/subretinal fluid volumes were maximum at baseline (intraretinal fluid: 21.5/76.6/107.1 nL; subretinal fluid 13.7/86/262.5 nL in the 1/3/6-mm area). Intraretinal fluid decreased to 5 nL at M1-M3 (1-mm) and increased to 11 nL (Y1) and 16 nL (Y5). Subretinal fluid decreased to a mean of 4 nL at M1-M3 (1-mm) and remained stable below 7 nL until Y5. Intraretinal fluid was the only variable that reflected VA change over time. Comparison with human expert readings confirmed an area under the curve of >0.9.

CONCLUSION: The Vienna Fluid Monitor can precisely quantify fluid volumes in optical coherence tomography images from clinical routine over 5 years. Automated tools will introduce precision medicine based on fluid guidance into real-world management of exudative disease, improving clinical outcomes while saving resources.

PMID:35994584 | DOI:10.1097/IAE.0000000000003557

IEEE Trans Pattern Anal Mach Intell. 2022 Aug 22;PP. doi: 10.1109/TPAMI.2022.3200745. Online ahead of print.

ABSTRACT

Photon-efficient imaging, which captures 3D images with single-photon sensors, has enabled a wide range of applications. However, two major challenges limit the reconstruction performance, i.e., the low photon counts accompanied by low signal-to-background ratio (SBR) and the multiple returns. In this paper, we propose a unified deep neural network that, for the first time, explicitly addresses these two challenges, and simultaneously recovers depth maps and intensity images from photon-efficient measurements. Starting from a general image formation model, our network is constituted of one encoder, where a non-local block is utilized to exploit the long-range correlations in both spatial and temporal dimensions of the raw measurement, and two decoders, which are designed to recover depth and intensity, respectively. Meanwhile, we investigate the statistics of the background noise photons and propose a noise prior block to further improve the reconstruction performance. The proposed network achieves decent reconstruction fidelity even under extremely low photon counts / SBR and heavy blur caused by the multiple-return effect, which significantly surpasses the existing methods. Moreover, our network trained on simulated data generalizes well to real-world imaging systems, which greatly extends the application scope of photon-efficient imaging in challenging scenarios with a strict limit on optical flux. Code is available at https://github.com/JiayongO-O/PENonLocal.

PMID:35994546 | DOI:10.1109/TPAMI.2022.3200745