Nevin Manimala

PLoS One. 2023 Aug 15;18(8):e0289511. doi: 10.1371/journal.pone.0289511. eCollection 2023.

ABSTRACT

BACKGROUND: Patient safety is a global concern. Safe and effective care can shorten hospital stays and prevent or minimize unintentional harm to patients. Therefore, it is necessary to continuously monitor and improve patient safety in all medical environments. This study is aimed at improving patient safety in gastroenterology departments.

METHODS: The study was carried out as action research. The participants were patients, nurses and doctors of the gastroenterology department of Ayatollah Taleghani Hospital in Tehran in 2021-2022. Data were collected using questionnaires (medication adherence tool, patient education effectiveness evaluation checklist, and medication evidence-based checklist), individual interviews and focus groups. The quantitative data analysis was done using SPSS (v.20) and qualitative data analysis was done through content analysis method using MAXQDA analytic pro 2022 software.

RESULTS: The majority of errors were related to medication and the patient’s fault due to their lack of education and prevention strategy were active supervision, modification of clinical processes, improvement of patient education, and promotion of error reporting culture. The findings of the research showed that the presence of an active supervisor led to the identification and prevention of more errors (P<0.01). Regarding the improvement of clinical processes, elimination of reworks can increase satisfaction in nurses (P<0.01). In terms of patient education, the difference was not statistically significant (P>0.01); however, the mean medication adherence score was significantly different (P<0.01).

CONCLUSION: The improvement strategies of patient safety in Gastroenterology department included the modification of ward monitoring processes, improving/modification clinical processes, improvement of patient education, and development of error reporting culture. Identifying inappropriate processes and adjusting them based on the opinion of the stakeholders, proper patient education regarding self-care, careful monitoring using appropriate checklists, and presence of a supervisor in the departments can be effective in reducing the incidence rate. A comprehensive error reporting program provides an opportunity for employees to report errors.

PMID:37582075 | DOI:10.1371/journal.pone.0289511

PLoS One. 2023 Aug 15;18(8):e0289735. doi: 10.1371/journal.pone.0289735. eCollection 2023.

ABSTRACT

BACKGROUND: Researchers find it difficult to distinguish between depression with ASD (Depress-wASD) and without ASD (Depression) in adult patients. We aimed to clarify the differences in brain connectivity between patients with depression with ASD and without ASD.

METHODS: From April 2017 to February 2019, 22 patients with suspected depression were admitted to the hospital for diagnosis or follow-up and met the inclusion criteria. The diagnosis was determined according to the Diagnostic and Statistical Manual of Mental Disorders-5 by skilled psychiatrists. The Hamilton Depression Rating Scale (HAM-D), Young Mania Raging Scale (YMRS), Mini-International Neuropsychiatric Interview, Parent-interview ASD Rating Scale-Text Revision (PARS-TR), and Autism-Spectrum Quotient-Japanese version (AQ-J) were used to assess the patients’ background and help with diagnosis. Resting-state functional magnetic resonance imaging (rs-fMRI) was performed using the 3-T-MRI system. rs-fMRI was processed using the CONN functional connectivity toolbox. Voxel-based morphometry was performed using structural images.

RESULTS: No significant difference was observed between the Depress-wASD and Depression groups using the HAM-D, YMRS, AQ-J, Intelligence Quotient (IQ), and verbal IQ results. rs-fMRI for the Depress-wASD group indicated a positive connection between the salience network (SN) and right supramarginal gyrus (SMG) and a negative connection between the SN and hippocampus and para-hippocampus than that for the Depression group. No significant structural differences were observed between the groups.

CONCLUSIONS: We identified differences in the SN involving the SMG and hippocampal regions between the Depress-wASD and Depression groups.

PMID:37582068 | DOI:10.1371/journal.pone.0289735

RMD Open. 2022 Sep;8(2):e002529. doi: 10.1136/rmdopen-2022-002529.

ABSTRACT

BACKGROUND: Although genome-wide association studies (GWASs) have identified more than 100 loci associated with rheumatoid arthritis (RA) susceptibility, the causal genes and biological mechanisms remain largely unknown.

METHODS: A cross-tissue transcriptome-wide association study (TWAS) using the unified test for molecular signaturestool was performed to integrate GWAS summary statistics from 58 284 individuals (14 361 RA cases and 43 923 controls) with gene-expression matrix in the Genotype-Tissue Expression project. Subsequently, a single tissue by using FUSION software was conducted to validate the significant associations. We also compared the TWAS with different gene-based methodologies, including Summary Data Based Mendelian Randomization (SMR) and Multimarker Analysis of Genomic Annotation (MAGMA). Further in silico analyses (conditional and joint analysis, differential expression analysis and gene-set enrichment analysis) were used to deepen our understanding of genetic architecture and comorbidity aetiology of RA.

RESULTS: We identified a total of 47 significant candidate genes for RA in both cross-tissue and single-tissue test after multiple testing correction, of which 40 TWAS-identified genes were verified by SMR or MAGMA. Among them, 13 genes were situated outside of previously reported significant loci by RA GWAS. Both TWAS-based and MAGMA-based enrichment analyses illustrated the shared genetic determinants among autoimmune thyroid disease, asthma, type I diabetes mellitus and RA.

CONCLUSION: Our study unveils 13 new candidate genes whose predicted expression is associated with risk of RA, providing new insights into the underlying genetic architecture of RA.

PMID:37582060 | DOI:10.1136/rmdopen-2022-002529

Opt Lett. 2023 Aug 15;48(16):4392-4395. doi: 10.1364/OL.498188.

ABSTRACT

The single-pixel imaging technique uses multiple patterns to modulate the entire scene and then reconstructs a two-dimensional (2-D) image from the single-pixel measurements. Inspired by the statistical redundancy of natural images that distinct regions of an image contain similar information, we report a highly compressed single-pixel imaging technique with a decreased sampling ratio. This technique superimposes an occluded mask onto modulation patterns, realizing that only the unmasked region of the scene is modulated and acquired. In this way, we can effectively decrease 75% modulation patterns experimentally. To reconstruct the entire image, we designed a highly sparse input and extrapolation network consisting of two modules: the first module reconstructs the unmasked region from one-dimensional (1-D) measurements, and the second module recovers the entire scene image by extrapolation from the neighboring unmasked region. Simulation and experimental results validate that sampling 25% of the region is enough to reconstruct the whole scene. Our technique exhibits significant improvements in peak signal-to-noise ratio (PSNR) of 1.5 dB and structural similarity index measure (SSIM) of 0.2 when compared with conventional methods at the same sampling ratios. The proposed technique can be widely applied in various resource-limited platforms and occluded scene imaging.

PMID:37582040 | DOI:10.1364/OL.498188

Opt Lett. 2023 Aug 15;48(16):4245-4248. doi: 10.1364/OL.495624.

ABSTRACT

We present an unsupervised learning denoising method, RepE (representation and enhancement), designed for nonlinear optical microscopy images, such as second harmonic generation (SHG) and two-photon fluorescence (TPEF). Addressing the challenge of effectively denoising images with various noise types, RepE employs an encoder network to learn noise-free representations and a reconstruction network to generate denoised images. It offers several key advantages, including its ability to (i) operate without restrictive statistic assumptions, (ii) eliminate the need for clean-noisy pairs, and (iii) requires only a few training images. Comparative evaluations on real-world SHG and TPEF images from esophageal cancer tissue slides (ESCC) demonstrate that our method outperforms existing techniques in image quality metrics. The proposed method provides a practical, robust solution for denoising nonlinear optical microscopy images, and it has the potential to be extended to other nonlinear optical microscopy modalities.

PMID:37582003 | DOI:10.1364/OL.495624

Opt Lett. 2023 Aug 15;48(16):4237-4240. doi: 10.1364/OL.491689.

ABSTRACT

Semiconductor lasers subjected to strong current modulation produce gain-switched optical pulse trains. These lasers can also produce pulse trains at sub-harmonic repetition rates relative to the driving current modulation. We experimentally observe, and numerically model, that these pulse trains can be interrupted by single-cycle extreme pulses whose characteristics and statistics are similar to rogue waves. Modeling indicates that drops in the circulating optical power in the optical cavity precede the appearance of extreme pulses. At the single photon level, the stochastic source terms in the optical field equation dominate the circulating optical power.

PMID:37582001 | DOI:10.1364/OL.491689

IEEE Trans Neural Netw Learn Syst. 2023 Aug 15;PP. doi: 10.1109/TNNLS.2023.3301711. Online ahead of print.

ABSTRACT

It is attractive to extract plausible 3-D information from a single 2-D image, and self-supervised learning has shown impressive potential in this field. However, when only monocular videos are available as training data, moving objects at similar speeds to the camera can disturb the reprojection process during training. Existing methods filter out some moving pixels by comparing pixelwise photometric error, but the illumination inconsistency between frames leads to incomplete filtering. In addition, existing methods calculate photometric error within local windows, which leads to the fact that even if an anomalous pixel is masked out, it can still implicitly disturb the reprojection process, as long as it is in the local neighborhood of a nonanomalous pixel. Moreover, the ill-posed nature of monocular depth estimation makes the same scene correspond to multiple plausible depth maps, which damages the robustness of the model. In order to alleviate the above problems, we propose: 1) a self-reprojection mask to further filter out moving objects while avoiding illumination inconsistency; 2) a self-statistical mask method to prevent the filtered anomalous pixels from implicitly disturbing the reprojection; and 3) a self-distillation augmentation consistency loss to reduce the impact of ill-posed nature of monocular depth estimation. Our method shows superior performance on the KITTI dataset, especially when evaluating only the depth of potential moving objects.

PMID:37581977 | DOI:10.1109/TNNLS.2023.3301711

JMIR Res Protoc. 2023 Aug 15;12:e48014. doi: 10.2196/48014.

ABSTRACT

BACKGROUND: Fungal-bacterial cocolonization and coinfections pose an emerging challenge among patients suspected of having pulmonary tuberculosis (PTB); however, the underlying pathogenic mechanisms and microbiome interactions are poorly understood. Understanding how environmental microbes, such as fungi and bacteria, coevolve and develop traits to evade host immune responses and resist treatment is critical to controlling opportunistic pulmonary fungal coinfections. In this project, we propose to study the coexistence of fungal and bacterial microbial communities during chronic pulmonary diseases, with a keen interest in underpinning fungal etiological evolution and the predominating interactions that may exist between fungi and bacteria.

OBJECTIVE: This is a protocol for a study aimed at investigating the metabolic and molecular ecological evolution of opportunistic pulmonary fungal coinfections through determining and characterizing the burden, etiological profiles, microbial communities, and interactions established between fungi and bacteria as implicated among patients with presumptive PTB.

METHODS: This will be a laboratory-based cross-sectional study, with a sample size of 406 participants. From each participant, 2 sputa samples (one on-spot and one early morning) will be collected. These samples will then be analyzed for both fungal and bacterial etiology using conventional metabolic and molecular (intergenic transcribed spacer and 16S ribosomal DNA-based polymerase chain reaction) approaches. We will also attempt to design a genome-scale metabolic model for pulmonary microbial communities to analyze the composition of the entire microbiome (ie, fungi and bacteria) and investigate host-microbial interactions under different patient conditions. This analysis will be based on the interplays of genes (identified by metagenomics) and inferred from amplicon data and metabolites (identified by metabolomics) by analyzing the full data set and using specific computational tools. We will also collect baseline data, including demographic and clinical history, using a patient-reported questionnaire. Altogether, this approach will contribute to a diagnostic-based observational study. The primary outcome will be the overall fungal and bacterial diagnostic profile of the study participants. Other diagnostic factors associated with the etiological profile, such as incidence and prevalence, will also be analyzed using univariate and multivariate schemes. Odds ratios with 95% CIs will be presented with a statistical significance set at P<.05.

RESULTS: The study has been approved by the Mbarara University Research Ethic Committee (MUREC1/7-07/09/20) and the Uganda National Council of Science and Technology (HS1233ES). Following careful scrutiny, the protocol was designed to enable patient enrollment, which began in March 2022 at Mbarara University Teaching Hospital. Data collection is ongoing and is expected to be completed by August 2023, and manuscripts will be submitted for publication thereafter.

CONCLUSIONS: Through this protocol, we will explore the metabolic and molecular ecological evolution of opportunistic pulmonary fungal coinfections among patients with presumptive PTB. Establishing key fungal-bacterial cross-kingdom synergistic relationships is crucial for instituting fungal bacterial coinfecting etiology.

TRIAL REGISTRATION: ISRCTN Registry ISRCTN33572982; https://tinyurl.com/caa2nw69.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/48014.

PMID:37581914 | DOI:10.2196/48014

JMIR Res Protoc. 2023 Aug 15;12:e45917. doi: 10.2196/45917.

ABSTRACT

BACKGROUND: Intimate partner violence (IPV) disproportionately affects people in low-and middle-income countries (LMICs), such as Nepal. Women experiencing IPV are at higher risk of developing depression, anxiety, and posttraumatic stress disorder. The shortage of trained frontline health care providers, coupled with stigma related to IPV and mental health disorders, fuels low service uptake among women experiencing IPV. The Domestic Violence Intervention (DeVI) combines the Problem Management Plus counseling program developed by the World Health Organization with a violence prevention component.

OBJECTIVE: This study aims to implement and evaluate the feasibility, acceptability, and effectiveness of DeVI in addressing psychological distress and enabling the secondary prevention of violence for women experiencing IPV.

METHODS: A parallel cluster-randomized trial will be conducted across 8 districts in Madhesh Province in Nepal, involving 24 health care facilities. The study will include women aged 18-49 years who are either nonpregnant or in their first trimester, have experienced IPV within the past 12 months, have a 12-item General Health Questionnaire (GHQ-12) score of 3 or more (indicating current mental health issues), and have lived with their husbands or in-laws for at least 6 months. A total sample size of 912 was estimated at 80% power and α<.05 statistical significance level to detect a 15% absolute risk reduction in the IPV frequency and a 50% reduction in the GHQ-12 score in the intervention arm. The health care facilities will be randomly assigned to either the intervention or the control arm in a 1:1 ratio. Women visiting the health care facilities in the intervention and control arms will be recruited into the respective arms. In total, 38 participants from each health care facility will be included in the trial to meet the desired sample size. Eligible participants allocated to either arm will be assessed at baseline and follow-up visits after 6, 17, and 52 weeks after baseline.

RESULTS: This study received funding in 2019. As of December 29, 2022, over 50% of eligible women had been recruited from both intervention and control sites. In total, 269 eligible women have been enrolled in the intervention arm and 309 eligible women in the control arm. The trial is currently in the recruitment phase. Data collection is expected to be completed by December 2023, after which data analysis will begin.

CONCLUSIONS: If the intervention proves effective, it will provide evidence of how nonspecialist mental health care providers can address the harmful effects of IPV in resource-constrained settings with a high burden of IPV, such as Nepal. The study findings could also contribute evidence for integrating similar services into routine health programs in LMICs to prevent IPV and manage mental health problems among women experiencing IPV.

TRIAL REGISTRATION: ClinicalTrials.gov NCT05426863; https://clinicaltrials.gov/ct2/show/NCT05426863.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/45917.

PMID:37581909 | DOI:10.2196/45917

Curr Environ Health Rep. 2023 Aug 15. doi: 10.1007/s40572-023-00406-7. Online ahead of print.

ABSTRACT

PURPOSE OF REVIEW: The volume of public health environmental justice (EJ) research produced by academic institutions increased through 2022. However, the methods used for evaluating EJ in exposure science and epidemiologic studies have not been catalogued. Here, we completed a scoping review of EJ studies published in 19 environmental science and epidemiologic journals from 2018 to 2021 to summarize research types, frameworks, and methods.

RECENT FINDINGS: We identified 402 articles that included populations with health disparities as a part of EJ research question and met other inclusion criteria. Most studies (60%) evaluated EJ questions related to socioeconomic status (SES) or race/ethnicity. EJ studies took place in 69 countries, led by the US (n = 246 [61%]). Only 50% of studies explicitly described a theoretical EJ framework in the background, methods, or discussion and just 10% explicitly stated a framework in all three sections. Among exposure studies, the most common area-level exposure was air pollution (40%), whereas chemicals predominated personal exposure studies (35%). Overall, the most common method used for exposure-only EJ analyses was main effect regression modeling (50%); for epidemiologic studies the most common method was effect modification (58%), where an analysis evaluated a health disparity variable as an effect modifier. Based on the results of this scoping review, current methods in public health EJ studies could be bolstered by integrating expertise from other fields (e.g., sociology), conducting community-based participatory research and intervention studies, and using more rigorous, theory-based, and solution-oriented statistical research methods.

PMID:37581863 | DOI:10.1007/s40572-023-00406-7