Category: Nevin Manimala

Sci Rep. 2023 Feb 9;13(1):2294. doi: 10.1038/s41598-023-29556-2.

ABSTRACT

Infections with enteric pathogens have a high mortality and morbidity burden, as well as significant social and economic costs. Poor water, sanitation, and hygiene (WASH) conditions are the leading risk factors for enteric infections, and prevention in low-income countries is still primarily focused on initiatives to improve access to improved WASH facilities. Rural communities in developing countries, on the other hand, have limited access to improved WASH services, which may result in a high burden of enteric infections. Limited information also exists about the prevalence of enteric infections and management practices among rural communities. Accordingly, this study was conducted to assess enteric infections and management practices among communities in a rural setting of northwest Ethiopia. A community-based cross-sectional study was conducted among 1190 randomly selected households in a rural setting of northwest Ethiopia. Data were collected using structured and pretested interviewers-administered questionnaire and spot-check observations. We used self-reports and medication history audit to assess the occurrence of enteric infections among one or more of the family members in the rural households. Multivariable binary logistic regression model was used to identify factors associated with enteric infections. Statistically significant association was declared on the basis of adjusted odds ratio with 95% confidence interval and p value < 0.05. Out of a total of 1190 households, 17.4% (95% CI: 15.1, 19.7%) of the households reported that one or more of the family members acquired one or more enteric infections in 12 months period prior to the survey and 470 of 6089 (7.7%) surveyed individuals had one or more enteric infections. The common enteric infections reported at household-level were diarrhea (8.2%), amoebiasis (4.1%), and ascariasis (3.9%). Visiting healthcare facilities (71.7%), taking medications without prescriptions (21.1%), and herbal medicine (4.5%) are the common disease management practices among rural households in the studied region. The occurrence of one or more enteric infections among one or more of the family members in rural households in 12 months period prior to the survey was statistically associated with presence of livestock (AOR: 2.24, 95% CI:1.06, 4.75) and households headed by uneducated mothers (AOR: 1.62, 95% CI: (1.18, 2.23). About one-fifth of the rural households in the studied region reported that one or more of the family members had one or more enteric infections. Households in the study area might acquire enteric infections from different risk factors, mainly poor WASH conditions and insufficient separation of animals including their feces from human domestic environments. It is therefore important to implement community-level interventions such as utilization of improved latrine, protecting water sources from contamination, source-based water treatment, containment of domestic animals including their waste, community-driven sanitation, and community health champion.

PMID:36759710 | DOI:10.1038/s41598-023-29556-2

Eye (Lond). 2023 Feb 9. doi: 10.1038/s41433-023-02442-x. Online ahead of print.

ABSTRACT

PURPOSE: Anticholinergic drugs are widely prescribed for many medical conditions. However, data on the association of anticholinergic burden with dry eye disease (DED) are limited. In this study, we aimed to examine the relationship between anticholinergic burden and DED.

METHODS: In this retrospective cohort study, we evaluated a total of 120 participants who underwent ophthalmological examination between February 2021 and February 2022. The drugs used by the patients in the last 2 months were recorded from the institute’s electronic data system. Anticholinergic burden was assessed using the Anticholinergic Cognitive Burden (ACB) scale.

RESULTS: The mean age of those patients was 59.0 ± 11.6 years and more than half (n = 33, 64.7%) were women. Patients with DED had significantly higher Charlson comorbidity index scores (p = 0.01), lower Schirmer test values (p = 0.01), higher Ocular Surface Disease Index (OSDI) scores (p = 0.01), and higher anticholinergic burden (p = 0.01). There was a statistically significant positive correlation between ACB and OSDI scores (r = 0.22, p = 0.02) and a negative correlation between ACB scores and Schirmer test values (r = -0.46, p = 0.01). After adjusting for potential confounding factors (age, gender, and comorbidities), each 1-point increase in anticholinergic burden was found to result in a 2.97-fold increase in the risk of DED (OR: 2.97, 95% confidence interval: 1.22-7.24, p = 0.02).

CONCLUSION: Anticholinergic burden appears to be associated with DED. Therefore, greater caution in prescribing anticholinergic drugs for adult patients may be important in reducing the rates of many adverse outcomes.

PMID:36759707 | DOI:10.1038/s41433-023-02442-x

Sci Rep. 2023 Feb 9;13(1):2359. doi: 10.1038/s41598-023-29453-8.

ABSTRACT

The evolvement in digital media and information technology over the past decades have purveyed the internet to be an effectual medium for the exchange of data and communication. With the advent of technology, the data has become susceptible to mismanagement and exploitation. This led to the emergence of Internet Security frameworks like Information hiding and detection. Examples of domains of Information hiding and detection are Steganography and steganalysis respectively. This work focus on addressing possible security breaches using Internet security framework like Information hiding and techniques to identify the presence of a breach. The work involves the use of Blind steganalysis technique with the concept of Machine Learning incorporated into it. The work is done using the Joint Photographic Expert Group (JPEG) format because of its wide use for transmission over the Internet. Stego (embedded) images are created for evaluation by randomly embedding a text message into the image. The concept of calibration is used to retrieve an estimate of the cover (clean) image for analysis. The embedding is done with four different steganographic schemes in both spatial and transform domain namely LSB Matching and LSB Replacement, Pixel Value Differencing and F5. After the embedding of data with random percentages, the first order, the second order, the extended Discrete Cosine Transform (DCT) and Markov features are extracted for steganalysis.The above features are a combination of interblock and intra block dependencies. They had been considered in this paper to eliminate the drawback of each one of them, if considered separately. Dimensionality reduction is applied to the features using Principal Component Analysis (PCA). Block based technique had been used in the images for better accuracy of results. The technique of machine learning is added by using classifiers to differentiate the stego image from a cover image. A comparative study had been during with the classifier names Support Vector Machine and its evolutionary counterpart using Particle Swarm Optimization. The idea of cross validation had also been used in this work for better accuracy of results. Further parameters used in the process are the four different types of sampling namely linear, shuffled, stratified and automatic and the six different kernels used in classification specifically dot, multi-quadratic, epanechnikov, radial and ANOVA to identify what combination would yield a better result.

PMID:36759703 | DOI:10.1038/s41598-023-29453-8

Sci Rep. 2023 Feb 9;13(1):2341. doi: 10.1038/s41598-023-29307-3.

ABSTRACT

We often express our thoughts through words, but thinking goes well beyond language. Here we focus on an elementary but basic thinking process, disjunction elimination, elicited by elementary visual scenes deprived of linguistic content, describing its neural and oculomotor correlates. We track two main components of a nonverbal deductive process: the construction of a logical representation (A or B), and its simplification by deduction (not A, therefore B). We identify the network active in the two phases and show that in the latter, but not in the former, it overlaps with areas known to respond to verbal logical reasoning. Oculomotor markers consistently differentiate logical processing induced by the construction of a representation, its simplification by deductive inference, and its maintenance when inferences cannot be drawn. Our results reveal how integrative logical processes incorporate novel experience in the flow of thoughts induced by visual scenes.

PMID:36759690 | DOI:10.1038/s41598-023-29307-3

Leukemia. 2023 Feb 9. doi: 10.1038/s41375-022-01784-x. Online ahead of print.

NO ABSTRACT

PMID:36759685 | DOI:10.1038/s41375-022-01784-x

Sci Rep. 2023 Feb 9;13(1):2311. doi: 10.1038/s41598-023-29554-4.

ABSTRACT

Transferring patients between emergency departments (EDs) is a complex but important issue in emergency care regionalization. Social network analysis (SNA) is well-suited to characterize the ED transfer pattern. We aimed to unravel the underlying transfer network structure and to identify key network metrics for monitoring network functions. This was a retrospective cohort study using the National Electronic Referral System (NERS) database in Taiwan. All interhospital ED transfers from 2014 to 2016 were included and transfer characteristics were retrieved. Descriptive statistics and social network analysis were used to analyze the data. There were a total of 218,760 ED transfers during the 3-year study period. In the network analysis, there were a total of 199 EDs with 9516 transfer ties between EDs. The network demonstrated a multiple hub-and-spoke, regionalized pattern, with low global density (0.24), moderate centralization (0.57), and moderately high clustering of EDs (0.63). At the ED level, most transfers were one-way, with low reciprocity (0.21). Sending hospitals had a median of 5 transfer-out partners [interquartile range (IQR) 3-7), while receiving hospitals a median of 2 (IQR 1-6) transfer-in partners. A total of 16 receiving hospitals, all of which were designated base or co-base hospitals, had 15 or more transfer-in partners. Social network analysis of transfer patterns between hospitals confirmed that the network structure largely aligned with the planned regionalized transfer network in Taiwan. Understanding the network metrics helps track the structure and process aspects of regionalized care.

PMID:36759680 | DOI:10.1038/s41598-023-29554-4

Sci Rep. 2023 Feb 9;13(1):2372. doi: 10.1038/s41598-023-27528-0.

ABSTRACT

Automated preprocessing methods are critically needed to process the large publicly-available EEG databases, but the optimal approach remains unknown because we lack data quality metrics to compare them. Here, we designed a simple yet robust EEG data quality metric assessing the percentage of significant channels between two experimental conditions within a 100 ms post-stimulus time range. Because of volume conduction in EEG, given no noise, most brain-evoked related potentials (ERP) should be visible on every single channel. Using three publicly available collections of EEG data, we showed that, with the exceptions of high-pass filtering and bad channel interpolation, automated data corrections had no effect on or significantly decreased the percentage of significant channels. Referencing and advanced baseline removal methods were significantly detrimental to performance. Rejecting bad data segments or trials could not compensate for the loss in statistical power. Automated Independent Component Analysis rejection of eyes and muscles failed to increase performance reliably. We compared optimized pipelines for preprocessing EEG data maximizing ERP significance using the leading open-source EEG software: EEGLAB, FieldTrip, MNE, and Brainstorm. Only one pipeline performed significantly better than high-pass filtering the data.

PMID:36759667 | DOI:10.1038/s41598-023-27528-0

Bioinspir Biomim. 2023 Feb 9. doi: 10.1088/1748-3190/acbad2. Online ahead of print.

ABSTRACT

The physiology of living organisms, such as living plants, is complex and particularly difficult to understand on a macroscopic, organism-holistic level. Among the many options to study plant physiology, electrical potential and tissue impedance are arguably simple measurement techniques to gather plant-level information. Despite the many possible uses, our research is exclusively driven by the idea of phytosensing, that is, interpreting living plants’ signals to learn information about surrounding environmental conditions. As ready-to-use plant-level physiological models are not available, we consider the plant as a blackbox and apply statistics and machine learning to automatically interpret measured signals. In simple plant experiments, we expose Zamioculcas zamiifolia and Solanum lycopersicum (tomato) to four different stimuli: wind, heat, red and blue light. We measure electrical potential and tissue impedance signals. Given these signals, we evaluate a large variety of methods from statistical discriminant analysis and from deep learning for the classification problem of determining the correct stimulus to which the plant was exposed. We identify a set of methods that successfully classify stimuli with good accuracy without a clear winner. The statistical approach is competitive, partially depending on data availability for the machine learning approach. Our extensive results show the feasibility of the blackbox approach and can be used in future research to select appropriate classifier techniques for a given use case. In our own future research, we will exploit these methods to drive a phytosensing approach for air pollution monitoring in urban areas.

PMID:36758242 | DOI:10.1088/1748-3190/acbad2

Biomed Phys Eng Express. 2023 Feb 9. doi: 10.1088/2057-1976/acbaa0. Online ahead of print.

ABSTRACT

The purpose of this work was to investigate by Monte Carlo method the adjustment of photon beams delivered by the medical LINear ACcelerator (LINAC) Elekta Synergy MLCi2. This study presents an optimization of the Gaussian distribution parameters of the accelerated electrons before the target simulated by two Monte Carlo codes and for three beams. The photon (X-ray) beam is produced by the interaction of accelerated electrons with the LINAC target. The electrons are accelerated by a potential difference created between the anode and the cathode of the gun and directed towards the target. In the Monte Carlo simulation, it is necessary to setup the spectrum parameters of the generated electrons to simulate the X-ray dose distribution. In this study, we modeled the LINAC geometry for photon beams 18MV and 6MV in cases Flattened (FF) and Flattening-Filter-Free (FFF). The Monte Carlo simulations are based on G4Linac_MT and GATE codes. The results of the optimized configurations determined after more than 20 tests for each beam energy show a very good agreement with the experimental measurements for different irradiation fields for the depth (PDD) and lateral (Profile) dose distribution. In all Monte Carlo calculations performed in this study, the statistical uncertainty is less than 2%. The results were also in very good agreement in terms of γ-index analysis, for the 3%/3mm and 2%/2mm criteria.

PMID:36758237 | DOI:10.1088/2057-1976/acbaa0

Am J Public Health. 2023 Feb 9:e1-e4. doi: 10.2105/AJPH.2022.307206. Online ahead of print.

ABSTRACT

Objectives. To examine the impact of school shootings on indicators of adolescent school safety in the United States. Methods. We linked 2009-2019 Youth Risk Behavior Survey data on 211 236 adolescents aged 14 to 18 years from 24 school districts with data on high school shootings from the Center for Homeland Defense and Security. We conducted 2-way fixed-effects logistic regression models to assess the impact of shootings on self-report of 3 indicators of school safety: avoiding school because of feeling unsafe, carrying a weapon at school, and being threatened or injured with a weapon at school. Results. High school shootings were associated with adolescents having 20% greater odds of avoiding school because of feeling unsafe (adjusted odd ratio [AOR] = 1.20; 95% confidence interval [CI] = 1.11, 1.29) than those who had not. Findings were slightly attenuated in sensitivity analyses that tested exposure to shootings at any school in the district or state. High school shootings were associated with a statistically nonsignificant (P = .08) elevated risk of carrying a weapon at school (AOR = 1.11; 95% CI = 0.99, 1.25). Conclusions. The negative ramifications of school shootings extend far beyond the event itself to adolescents’ concerns about school safety. (Am J Public Health. Published online ahead of print February 9, 2023:e1-e4. https://doi.org/10.2105/AJPH.2022.307206).

PMID:36758203 | DOI:10.2105/AJPH.2022.307206