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This study examined the effectiveness of an integrated care pathway (ICP), including a medication algorithm, to treat agitation associated with dementia.
Analyses of data (both prospective and retrospective) collected during routine clinical care.
Geriatric Psychiatry Inpatient Unit.
Patients with agitation associated with dementia (n = 28) who were treated as part of the implementation of the ICP and those who received treatment-as-usual (TAU) (n = 28) on the same inpatient unit before the implementation of the ICP. Two control groups of patients without dementia treated on the same unit contemporaneously to the TAU (n = 17) and ICP groups (n = 36) were included to account for any secular trends.
Cohen Mansfield Agitation Inventory (CMAI), Neuropsychiatric Inventory Questionnaire (NPIQ), and assessment of motor symptoms were completed during the ICP implementation. Chart review was used to obtain length of inpatient stay and rates of psychotropic polypharmacy.
Patients in the ICP group experienced a reduction in their scores on the CMAI and NPIQ and no changes in motor symptoms. Compared to the TAU group, the ICP group had a higher chance of an earlier discharge from hospital, a lower rate of psychotropic polypharmacy, and a lower chance of having a fall during hospital stay. In contrast, these outcomes did not differ between the two control groups.
These preliminary results suggest that an ICP can be used effectively to treat agitation associated with dementia in inpatients. A larger randomized study is needed to confirm these results.
Girstmair [‘On an irreducibility criterion of M. Ram Murty’, Amer. Math. Monthly112(3) (2005), 269–270] gave a generalisation of Ram Murty’s irreducibility criterion. We further generalise these criteria.
We report a familial cluster of 24 individuals infected with severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). The index case had a travel history and spent 24 days in the house before being tested and was asymptomatic. Physical overcrowding in the house provided a favourable environment for intra-cluster infection transmission. Restriction of movement of family members due to countrywide lockdown limited the spread in community. Among the infected, only four individuals developed symptoms. The complete genome sequences of SARS-CoV-2 was retrieved using next-generation sequencing from eight clinical samples which demonstrated a 99.99% similarity with reference to Wuhan strain and the phylogenetic analysis demonstrated a distinct cluster, lying in the B.6.6 pangolin lineage.
The COVID-19 pandemic has placed significant strain on emergency departments (EDs) that were not designed to care for many patients who may be highly contagious. This report outlines how a busy urban ED was adapted to prepare for COVID-19 via 3 primary interventions: (1) creating an open-air care space in the ambulance bay to cohort, triage, and rapidly test patients with suspected COVID-19, (2) quickly constructing temporary doors on all open treatment rooms, and (3) adapting and expanding the waiting room. This description serves as a model by which other EDs can repurpose their own care spaces to help ensure safety of their patients and health care workers.
Digital surveillance has shown mixed results as a supplement to traditional surveillance. Google Trends™ (GT) (Google, Mountain View, CA, United States) has been used for digital surveillance of H1N1, Ebola and MERS. We used GT to correlate the information seeking on COVID-19 with number of tests and cases in India.
Data was obtained on daily tests and cases from WHO, ECDC and covid19india.org. We used a comprehensive search strategy to retrieve GT data on COVID-19 related information-seeking behavior in India between January 1 and May 31, 2020 in the form of relative search volume (RSV). We also used time-lag correlation analysis to assess the temporal relationships between RSV and daily new COVID-19 cases and tests.
GT RSV showed high time-lag correlation with both daily reported tests and cases for the terms “COVID 19,” “COVID,” “social distancing,” “soap,” and “lockdown” at the national level. In 5 high-burden states, high correlation was observed for these 5 terms along with “Corona.” Peaks in RSV, both at the national level and in high-burden states corresponded with media coverage or government declarations on the ongoing pandemic.
The correlation observed between GT data and COVID-19 tests/cases in India may be either due to media-coverage-induced curiosity, or health-seeking curiosity.
Aortopulmonary window is a rare congenital heart defect. Left main coronary artery extrinsic compression by an enlarged pulmonary artery is a rare complication and a potential cause for chest pain and sudden cardiac death in patients with pulmonary hypertension. Here, we present the case of a 14-year-old boy with a large aortopulmonary window who was planned for a device closure, but during the procedure, he developed ST-T segment changes while the device was being deployed, and hence the procedure was abandoned. The boy subsequently underwent a successful surgical closure thereafter.
To compare the prevalence of select cardiovascular risk factors (CVRFs) in patients with mild cognitive impairment (MCI) versus lifetime history of major depression disorder (MDD) and a normal comparison group using baseline data from the Prevention of Alzheimer’s Dementia with Cognitive Remediation plus Transcranial Direct Current Stimulation (PACt-MD) study.
Baseline data from a multi-centered intervention study of older adults with MCI, history of MDD, or combined MCI and history of MDD (PACt-MD) were analyzed.
Community-based multi-centered study based in Toronto across 5 academic sites.
Older adults with MCI, history of MDD, or combined MCI and history of MDD and healthy controls.
We examined the baseline distribution of smoking, hypertension and diabetes in three groups of participants aged 60+ years in the PACt-MD cohort study: MCI (n = 278), MDD (n = 95), and healthy older controls (n = 81). Generalized linear models were fitted to study the effect of CVRFs on MCI and MDD as well as neuropsychological composite scores.
A higher odds of hypertension among the MCI cohort compared to healthy controls (p < .05) was noted in unadjusted analysis. Statistical significance level was lost on adjusting for age, sex and education (p > .05). A history of hypertension was associated with lower performance in composite executive function (p < .05) and overall composite neuropsychological test score (p < .05) among a pooled cohort with MCI or MDD.
This study reinforces the importance of treating modifiable CVRFs, specifically hypertension, as a means of mitigating cognitive decline in patients with at-risk cognitive conditions.
In this paper, an eight-shaped polarization-dependent electromagnetic bandgap (ES-PDEBG) structure is proposed. The unit cell of ES-PDEBG structure consists of an outer eight-shaped EBG patch with two inner square patches and three vias. Surface wave bandgap and reflection phase characteristics have been studied for the proposed structure. From the measurement results, two surface wave bandgaps with center frequencies 3.42 and 5.88 GHz are observed along the X-direction, and one surface wave bandgap with center frequency 3.69 GHz is observed along the Y-direction. The refection phase bandgap of the proposed structure is centered at 5.61 and 3.31 GHz for x- and y-polarized incident plane waves, respectively. Furthermore, the application of the proposed structure as polarization reflector is presented. The study demonstrates that the structure can act as dual-band in-polarization reflector for circularly polarized waves. In addition, incident linearly polarized waves are reflected as circularly polarized waves in four operating bands.
Appropriate screening is integral to the early diagnosis and management of Alzheimer’s Dementia (AD). The Paired Associates Learning (PAL) task is a digital cognitive task that is free of cultural, language, and educational biases. This study examined the association between the PAL task performance and global cognition and the usefulness of the PAL task as a screening tool for AD.
Twenty-five participants with AD and 22 healthy comparators (HC) were included. The Cambridge Neuropsychological Test Automated Battery PAL task and the Montreal Cognitive Assessment (MoCA) were used to assess cognition. We assessed the relationship between the PAL task and MoCA performance using Pearson correlation and linear regression. We also examined the PAL task’s ability to distinguish between AD and HC participants using Receiver Operating Characteristic curve (ROC) analysis.
MoCA Total Score had a strong positive correlation with PAL Stages Completed score (r = 0.8, p < 0.001), and a strong negative correlation with PAL Total Errors (adjusted) score (r = −0.9, p < 0.001). Further, PAL Total Errors (adjusted) score predicted the MoCA Total Score (F (4, 46) = 37.2, p < 0.001). On ROC analysis, PAL Total Errors (adjusted) score cut-off of 54 errors had 92% sensitivity and 86% specificity to detect AD.
Performance on the PAL task is highly associated with global cognition. Further, the PAL task can differentiate patients with AD from HCs with high sensitivity and specificity. Thus, the PAL task may hold potential usage as an easy-to-administer screening tool for AD.
Pilot randomized double-blind-controlled trial of repetitive paired associative stimulation (rPAS), a paradigm that combines transcranial magnetic stimulation (TMS) of the dorsolateral prefrontal cortex (DLPFC) with peripheral median nerve stimulation.
To study the impact of rPAS on DLPFC plasticity and working memory performance in Alzheimer’s disease (AD).
Thirty-two patients with AD (females = 16), mean (SD) age = 76.4 (6.3) years were randomized 1:1 to receive a 2-week (5 days/week) course of active or control rPAS. DLPFC plasticity was assessed using single session PAS combined with electroencephalography (EEG) at baseline and on days 1, 7, and 14 post-rPAS. Working memory and theta–gamma coupling were assessed at the same time points using the N-back task and EEG.
There were no significant differences between the active and control rPAS groups on DLPFC plasticity or working memory performance after the rPAS intervention. There were significant main effects of time on DLPFC plasticity, working memory, and theta–gamma coupling, only for the active rPAS group. Further, on post hoc within-group analyses done to generate hypotheses for future research, as compared to baseline, only the rPAS group improved on post-rPAS day 1 on all three indices. Finally, there was a positive correlation between working memory performance and theta–gamma coupling.
This study did not show a beneficial effect of rPAS for DLPFC plasticity or working memory in AD. However, post hoc analyses showed promising results favoring rPAS and supporting further research on this topic. (Clinicaltrials.gov-NCT01847586)
In this paper, a novel design of ultra-wide stop-band single-side single-layer frequency selective surface (FSS) is presented. The unit cell of the proposed FSS is designed using the combination of conventional square loop and cross (CSLC). To enhance the bandwidth of this structure, an additional cross is inserted in all the four quadrants of CSLC. The stop-band transmission bandwidth assuming −10 dB threshold is found to be 128.94% (2.16–10 GHz) which is 34.33% more as compared to the bandwidth of CSLC. The unit cell with a dimension of 16 × 16 mm2 is printed on one side of an FR4 substrate. The design is fabricated and the measured results are found to be in good agreement with the simulated results. The design provides excellent stability for both transverse magnetic and transverse electric polarizations. The design is very flexible, where any resonant frequency can be achieved by changing the length of unit cell. The design is useful in many applications such as antenna gain enhancement, electromagnetic wave shielding for Wi-Fi/5G systems, and other Internet of Things-based applications.
During the coronavirus disease (COVID-19) pandemic, Indian nationals evacuated from Iran were quarantined at Jaisalmer, Rajasthan. We wished to study the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in this closed population.
A basic susceptible, exposed, infected, and removed (SEIR) compartmental model was developed using the daily stepwise approach in Microsoft Excel. An advanced model using standard differential equations in Python software version 3.6 was used to estimate R0 based on model fit to actual data.
Forty-eight SARS-CoV-2 infections were found among the 474 quarantined individuals. Out of these, 44 (92%) were asymptomatic. R0 for the overall duration was found to be 2.29 (95% CI: 1.84–2.78). Male gender and age ≥ 60 years were associated with SARS-CoV-2 infection (RR = 4.33, 95% CI: 2.07–9.05 and 5.32, 95% CI: 3.13–9.04, respectively). Isolation of infected individuals and stricter quarantine of remaining individuals reduced the R0 from 2.41 initially to 1.17 subsequently.
R0 value was found comparable to the earlier studies indicating similar transmission dynamics among quarantined individuals in India. Universal testing and prompt isolation of infected individuals reduced the transmission of SARS-CoV-2. Smaller group sizes should be preferred to large groups during facility-based quarantine in evacuation situations. The role of asymptomatic individuals appears to be strong in SARS-CoV-2 transmission within closed populations.
Background: Healthcare-associated infections (HAIs) are a major global threat to patient safety. Systematic surveillance is crucial for understanding HAI rates and antimicrobial resistance trends and to guide infection prevention and control (IPC) activities based on local epidemiology. In India, no standardized national HAI surveillance system was in place before 2017. Methods: Public and private hospitals from across 21 states in India were recruited to participate in an HAI surveillance network. Baseline assessments followed by trainings ensured that basic microbiology and IPC implementation capacity existed at all sites. Standardized surveillance protocols for central-line–associated bloodstream infections (CLABSIs) and catheter-associated urinary tract infections (CAUTIs) were modified from the NHSN for the Indian context. IPC nurses were trained to implement surveillance protocols. Data were reported through a locally developed web portal. Standardized external data quality checks were performed to assure data quality. Results: Between May 2017 and April 2019, 109 ICUs from 37 hospitals (29 public and 8 private) enrolled in the network, of which 33 were teaching hospitals with >500 beds. The network recorded 679,109 patient days, 212,081 central-line days, and 387,092 urinary catheter days. Overall, 4,301 bloodstream infection (BSI) events and 1,402 urinary tract infection (UTI) events were reported. The network CLABSI rate was 9.4 per 1,000 central-line days and the CAUTI rate was 3.4 per 1,000 catheter days. The central-line utilization ratio was 0.31 and the urinary catheter utilization ratio was 0.57. Moreover, 3,542 (73%) of 4,742 pathogens reported from BSIs and 868 (53%) of 1,644 pathogens reported from UTIs were gram negative. Also, 1,680 (26.3%) of all 6,386 pathogens reported were Enterobacteriaceae. Of 1,486 Enterobacteriaceae with complete antibiotic susceptibility testing data reported, 832 (57%) were carbapenem resistant. Of 951 Enterobacteriaceae subjected to colistin broth microdilution testing, 62 (7%) were colistin resistant. The surveillance platform identified 2 separate hospital-level HAI outbreaks; one caused by colistin-resistant K. pneumoniae and another due to Burkholderia cepacia. Phased expansion of surveillance to additional hospitals continues. Conclusions: HAI surveillance was successfully implemented across a national network of diverse hospitals using modified NHSN protocols. Surveillance data are being used to understand HAI burden and trends at the facility and national levels, to inform public policy, and to direct efforts to implement effective hospital IPC activities. This network approach to HAI surveillance may provide lessons to other countries or contexts with limited surveillance capacity.
This study assessed the tobacco smoking-associated risk for tuberculous pleural effusion (TPE) in India. Ninety-two patients with TPE and 184 controls were randomly selected and assessed regarding their tobacco-smoking status and type, quantity and duration of tobacco used. Odds ratios (ORs) for the association of smoking cigarette, beedi and cigarette or beedi with TPE were 19.22 (p < 0.0001), 2.89 (p = 0.0006) and 4.57 (p < 0.0001) respectively. ORs for developing TPE increased with an increase in beedi/cigarette consumption, duration and pack years of smoking (p < 0.001 each). TPE was significantly associated with confounding risk factors viz., regular alcohol use (OR = 1.89, p = 0.019), history of contact with tuberculosis (TB) patient (OR = 8.07, p < 0.0001), past history of TB (OR = 22.31, p < 0.0001), family history of TB (OR = 9.05, p = 0.0002) and underweight (OR = 3.73, p = 0.0009). Smoking (OR = 3.07, p < 0.001), regular alcohol use (OR = 2.10, p = 0.018), history of contact with TB patient (OR = 4.01, p = 0.040), family history of TB (OR = 10.80, p = 0.001) and underweight (OR = 5.04, p < 0.001) were independently associated with TPE. Thus, both cigarette- and beedi-smoking have a significant association with TPE. The risk for TPE in tobacco smokers is dose- and duration-dependent.
Data mining is a process of finding correlations and collecting and analysing a huge amount of data in a database to discover patterns or relationships. Flight delay creates significant problems in the present aviation system. Data mining techniques are desired for analysing the performance in which micro-level causes propagate to make system-level patterns of delay. Analysing flight delays is very difficult – both when looking from a historical view as well as when estimating delays with forecast demand. This paper proposes using Decision Tree (DT), Support Vector Machine (SVM), Naive Bayesian (NB), K-nearest neighbour (KNN) and Artificial Neural Network (ANN) to study and analyse delays among aircrafts. The performance of different data mining methods is found in the different regions of the updated datasets on these classifiers. Finally, the result shows a significant variation in the performance of different data mining methods and feature selection for this problem. This paper aims to deal with how data mining techniques can be used to understand difficult aircraft system delays in aviation. Our aim is to develop a classification model for studying and reducing delay using different data mining methods and, in this manner, to show that DT has a greater classification accuracy. The different feature selectors are used in this study in order to reduce the number of initial attributes. Our results clearly demonstrate the value of DT for analysing and visualising how system-level effects happen from subsystem-level causes.
The paper presents a new coplanar waveguide (CPW)-fed rectangular patch antenna with a square-shaped ground plane that can be employed in modern advanced navigation systems. For realizing broad impedance bandwidth in the proposed antenna, a wide slot is introduced in the square ground plane and the rectangular patch is shifted toward the left edge of the ground surface. In addition, by means of introducing square-shaped stubs near the left and right edge of the ground plane, the circular polarization is achieved at L1, L2, and L5 satellite bands. As per the simulation results, the proposed antenna provides a wide impedance bandwidth (S11<−10 dB) of 123% (1.12–4.72 GHz) and 3 dB axial ratio bandwidth of 11% (1.15–1.29 GHz) and 18% (1.5–1.8 GHz) suitable for multipurpose wireless applications. The designed single feed circularly polarized antenna is low profile, small size, light weight and easily integrable with other high-frequency communication devices. To validate radiation performance of the proposed structure, the antenna is fabricated and integrated with the commercially available Global Positioning System (GPS) receiver and it is found that the measured values are in close agreement with the desired results.
In this work, we focus on the task of open-type relation argument extraction (ORAE): given a corpus, a query entity Q, and a knowledge base relation (e.g., “Q authored notable work with title X”), the model has to extract an argument of non-standard entity type (entities that cannot be extracted by a standard named entity tagger, for example, X: the title of a book or a work of art) from the corpus. We develop and compare a wide range of neural models for this task yielding large improvements over a strong baseline obtained with a neural question answering system. The impact of different sentence encoding architectures and answer extraction methods is systematically compared. An encoder based on gated recurrent units combined with a conditional random fields tagger yields the best results. We release a data set to train and evaluate ORAE, based on Wikidata and obtained by distant supervision.