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Nevin Manimala Statistics

Dynamic tear meniscus parameters in complete blinking: insights into dry eye assessment

Int J Ophthalmol. 2023 Dec 18;16(12):1911-1918. doi: 10.18240/ijo.2023.12.01. eCollection 2023.

ABSTRACT

AIM: To investigate the relationship between dynamic tear meniscus parameters and dry eye using an automated tear meniscus segmentation method.

METHODS: The analysis of tear meniscus videos captured within 5s after a complete blink includes data from 38 participates. By processing video data, several key parameters including the average height of the tear meniscus at different lengths, the curvature of the tear meniscus’s upper boundary, and the total area of the tear meniscus in each frame were calculated. The effective values of these dynamic parameters were then linearly fitted to explore the relationship between their changing trends and dry eye disease.

RESULTS: In 94.74% of the samples, the average height of central tear meniscus increased over time. Moreover, 97.37% of the samples exhibited an increase in the overall tear meniscus height (TMH) and area from the nasal to temporal side. Notably, the central TMH increased at a faster rate compared to the nasal side with the temporal side showing the slowest ascent. Statistical analysis indicates that the upper boundary curvature of the whole tear meniscus as well as the tear meniscus of the nasal side (2, 3, and 4 mm) aid in identifying the presence of dry eye and assessing its severity.

CONCLUSION: This study contributes to the understanding of tear meniscus dynamics as potential markers for dry eye, utilizing an automated and non-invasive approach that has implications for clinical assessment.

PMID:38111923 | PMC:PMC10700063 | DOI:10.18240/ijo.2023.12.01

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Nevin Manimala Statistics

Club cell protein (CC16) in serum as an effect marker for small airway epithelial damage caused by diesel exhaust and blasting fumes in potash mining

Int Arch Occup Environ Health. 2023 Dec 18. doi: 10.1007/s00420-023-02035-x. Online ahead of print.

ABSTRACT

OBJECTIVE: The effect marker club cell protein (CC16) is secreted by the epithelium of the small respiratory tract into its lumen and passes into the blood. Increased amounts of CC16 in serum are observed during acute epithelial lung injury due to air pollutants. CC16 in serum was determined as part of this cross-sectional study in underground potash miners on acute and chronic health effects from exposures to diesel exhaust and blasting fumes.

METHODS: Nitrogen oxides, carbon monoxide, and diesel particulate matter were measured in 672 workers at a German potash mining site on a person-by-person basis over an early shift or midday shift, together with CC16 serum concentrations before and after the respective shift. CC16 concentrations and CC16 shift-differences were evaluated with respect to personal exposure measurements and other quantitative variables by Spearman rank correlation coefficients. CC16 shift-differences were modeled using multiple linear regression. Above-ground workers as reference group were compared to the exposed underground workers.

RESULTS: Serum concentrations of CC16 were influenced by personal characteristics such as age, smoking status, and renal function. Moreover, they showed a circadian rhythm. While no statistically significant effects of work-related exposure on CC16 concentrations were seen in never smokers, such effects were evident in current smokers.

CONCLUSION: The small airways of current smokers appeared to be vulnerable to the combination of measured work-related exposures and individual exposure to smoking. Therefore, as health protection of smokers exposed to diesel exhaust and blasting fumes, smoking cessation is strongly recommended.

PMID:38110551 | DOI:10.1007/s00420-023-02035-x

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Nevin Manimala Statistics

Mushroom poisoning outbreaks in Guizhou Province, China: a prediction study using SARIMA and Prophet models

Sci Rep. 2023 Dec 18;13(1):22517. doi: 10.1038/s41598-023-49095-0.

ABSTRACT

Mushroom poisoning is a public health concern worldwide that not only harms the physical and mental health of those who are poisoned but also increases the medical and financial burden on families and society. The present study aimed to describe and analyze the current situations and factors influencing mushroom poisoning outbreaks in Guizhou province, Southwest China, between January 2012 and June 2022, and to predict the future trends of its occurrence. Our study provides a basis for the rational formulation of prevention and control and medical resource allocation policies for mushroom poisoning. The epidemiological characteristics and factors influencing mushroom poisoning incidence were analyzed using descriptive epidemiological methods and the chi-squared test, respectively. Then, future occurrence trends were predicted using the SARIMA and Prophet models. In total, 1577 mushroom poisoning incidents were recorded in Guizhou Province, with 7347 exposures, 5497 cases, 3654 hospitalizations, and 93 fatalities. The mortality rate was 4.45% in 1 ~ 6 years higher than other age groups. There were notable geographic and seasonal characteristics, with the number of occurrences much higher in rural areas (1198) than in cities (379), and poisoning cases were more common during the rainy season (June to September). The mortality rate of household poisoning cases was 1.86%, with the most deaths occurring in households. Statistically significant differences were observed in the incidence across various cities, periods, and poisoning locations (P < 0.05). Both models had advantages and disadvantages for prediction. Nevertheless, the SARIMA model had better overall prediction results than the Prophet model (R > 0.9, the residual plot of the prediction results was randomly distributed, and RMSESARIMA < RMSEProphet). However, the prediction result plot of the Prophet model was more explanatory than the SARIMA model and could visualize overall and seasonal trends. Both models predicted that the prevalence of mushroom poisoning would continue to increase in the future; however, the number of fatalities is generally declining. Seasonal patterns indicated that a high number of deaths from gooseberry mushroom poisoning occurred in October. The epidemiological trends of mushroom poisoning remain severe, and health education on related knowledge must be strengthened in rural areas, with June to October as the key prevention and control phase. Further, medical treatment of mushroom poisoning cases with clinical symptoms should pay attention to inquiries to check whether the mushroom is similar in appearance to the Amanita, particularly in October.

PMID:38110518 | DOI:10.1038/s41598-023-49095-0

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Nevin Manimala Statistics

Multiplicative effect of frailty and obesity on postoperative mortality following spine surgery: a deep dive into the frailty, obesity, and Clavien-Dindo dynamic

Int J Obes (Lond). 2023 Dec 18. doi: 10.1038/s41366-023-01423-0. Online ahead of print.

ABSTRACT

BACKGROUND/OBJECTIVES: Obesity is a global health challenge that affects a large proportion of adults worldwide. Obesity and frailty pose considerable health risks due to their potential to interact and amplify one another’s negative effects. Therefore, we sought to compare the discriminatory thresholds of the risk analysis index (RAI), 5-factor modified frailty index (m-FI-5) and patient age for the primary endpoint of postoperative mortality.

SUBJECTS/METHODS: We included spine surgery patients ≥18 years old, from the American College of Surgeons National Quality Improvement program database from 2012-2020, that were classified as obese. We performed receiver operating characteristic curve analysis to compare the discrimination threshold of RAI, mFI-5, and patient age for postoperative mortality. Proportional hazards risk-adjusted regressions were performed, and Hazard ratios and corresponding 95% Confidence intervals (CI) are reported.

RESULTS: Overall, there were 149 163 patients evaluated, and in the ROC analysis for postoperative mortality, RAI showed superior discrimination C-statistic 0.793 (95%CI: 0.773-0.813), compared to mFI-5 C-statistic 0.671 (95%CI 0.650-0.691), and patient age C-statistic 0.686 (95%CI 0.666-0.707). Risk-adjusted analyses were performed, and the RAI had a stepwise increasing effect size across frailty strata: typical patients HR 2.55 (95%CI 2.03-3.19), frail patients HR 3.48 (95%CI 2.49-4.86), and very frail patients HR 4.90 (95%CI 2.87-8.37). We found increasing postoperative mortality effect sizes within Clavein-Dindo complication strata, consistent across obesity categories, exponentially increasing with frailty, and multiplicatively enhanced within CD, frailty and obesity strata.

CONCLUSION: In this study of 149 163 patients classified as obese and undergoing spine procedures in an international prospective surgical database, the RAI demonstrated superior discrimination compared to the mFI-5 and patient age in predicting postoperative mortality risk. The deleterious effects of frailty and obesity were synergistic as their combined effect predicted worse outcomes.

PMID:38110501 | DOI:10.1038/s41366-023-01423-0

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Nevin Manimala Statistics

Prevalence of tuberculosis infection among patients with Takayasu arteritis: a meta-analysis of observational studies

Sci Rep. 2023 Dec 18;13(1):22481. doi: 10.1038/s41598-023-49998-y.

ABSTRACT

To clarify the risk of tuberculosis (TB) infection in patients with Takayasu arteritis (TAK). In this study, we conducted a comprehensive search across multiple databases, including PubMed, Web of Science, Embase, Cochrane, and Medline, from the inception of the Literature Library to May 16, 2023. Using a specific set of keywords, including “Takayasu Arteritis”, “Tuberculosis”, and “Mycobacterium tuberculosis”, the main objective of this search was to identify all relevant observational studies, including case-control studies, cohort studies, and cross-sectional studies, that report the prevalence of TB in individuals diagnosed with TAK. Two independent evaluators rigorously screened the studies, extracted data, and assessed the study quality using the Joanna Briggs Institute (JBI) critical appraisal tools. Statistical analyses were conducted using R software version 4.3.0, which allowed for the synthesis of prevalence and subgroup analyses. Subgroup analyses were stratified based on quality scores, World Health Organization regional categorizations, and TB categories. Assessment of publication bias was performed using a funnel plot. The study included a total of 30 studies with 5548 participants. The findings showed that individuals with TAK exhibited an average prevalence of TB infection at 31.27% (95% CI 20.48-43.11%). Significantly, the prevalence of TB infection demonstrated notable regional disparities, ranging from 16.93% (95% CI 7.71-28.76%) in the Western Pacific Region to 63.58% (95% CI 35.70-87.66%) in the African Region. Moreover, the study revealed that patients with TAK displayed a high prevalence of latent TB infection (LTBI) at 50.01% (95% CI 31.25-68.77%) and active TB at 14.40% (95% CI 9.03-20.68%). The high heterogeneity observed in the data highlights significant variability in TB infection rates among the populations studied, with the African Region exhibiting the highest rates. The study concludes that there is a high prevalence of TB infection in the TAK population, with regional variations. Consideration should be given to implementing rigorous TB screening measures and preventive interventions specifically tailored for the TAK population.

PMID:38110470 | DOI:10.1038/s41598-023-49998-y

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Nevin Manimala Statistics

Lymphocyte subsets and inflammatory factors as predictors of immunotherapy efficacy in patients with hepatocellular carcinoma

Sci Rep. 2023 Dec 18;13(1):22480. doi: 10.1038/s41598-023-49810-x.

ABSTRACT

We aimed to investigate the correlation between lymphocyte subpopulations expressing inhibitor receptors, IL-6 levels, and the efficacy of immunotherapy in patients with hepatocellular carcinoma. Blood samples were prospectively collected before and after immunotherapy from patients with intermediate and advanced hepatocellular carcinoma who were treated with immunotherapy at the Fifth Medical Center of the PLA General Hospital from August 2022 to October 2023. According to the efficacy of the patients, patients were divided into effective and ineffective groups, with 40 in the effective group and 44 in the ineffective group. We compared changes in lymphocyte subsets and IL-6 levels between the two groups. Optimal cut-off value was determined using ROC curves. Then, patients were categorized into high and low groups based on cut-off value, and the disease control rates and progression free survival were compared. Before immunotherapy, there were no significant differences in the baseline levels of lymphocyte subsets (PD1 + TIM3 + T/T, TIGIT + T/T, TIM3 + T/T, CTLA4 + T/T, LAG3 + T/T, PD1 + T/T) and IL-6 between the two groups (P > 0.05). After immunotherapy, the levels of PD1 + TIM3 + T/T, TIGIT + T/T, and IL-6 in the effective group were lower than those in the ineffective group and these differences were statistically significant (P = 0.001, P = 0.008, P = 0.000). However, the levels of other lymphocyte subsets showed no significant difference. Using the ROC curve to assess efficacy prediction, PD1 + TIM3 + T/T, TIGIT + T/T and IL-6 demonstrated high predictive ability (AUC = 0.79, AUC = 0.81, AUC = 0.78). The predictive value of efficacy was further improved when all three factors were combined (AUC = 0.92, P = 0.000). Based on the ROC curve, we identified optimal cut-off value for three factors. Notably, patients with values below the optimal cut-off value had higher disease control rate and progression free survival. The levels of PD1 + TIM3 + T/T, TIGIT + T/T, and IL-6 after 2 cycles of immunotherapy may serve as predictors of treatment efficacy in patients with hepatocellular carcinoma.

PMID:38110467 | DOI:10.1038/s41598-023-49810-x

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Nevin Manimala Statistics

Using spectral and temporal filters with EEG signal to predict the temporal lobe epilepsy outcome after antiseizure medication via machine learning

Sci Rep. 2023 Dec 18;13(1):22532. doi: 10.1038/s41598-023-49255-2.

ABSTRACT

Epilepsy is a neurological disorder in which the brain is transiently altered. Predicting outcomes in epilepsy is essential for providing feedback that can foster improved outcomes in the future. This study aimed to investigate whether applying spectral and temporal filters to resting-state electroencephalography (EEG) signals could improve the prediction of outcomes for patients taking antiseizure medication to treat temporal lobe epilepsy (TLE). We collected EEG data from a total of 46 patients (divided into a seizure-free group (SF, n = 22) and a non-seizure-free group (NSF, n = 24)) with TLE and retrospectively reviewed their clinical data. We segmented spectral and temporal ranges with various time-domain features (Hjorth parameters, statistical parameters, energy, zero-crossing rate, inter-channel correlation, inter-channel phase locking value and spectral information derived from Fourier transform, Stockwell transform, and wavelet transform) and compared their performance by applying an optimal frequency strategy, an optimal duration strategy, and a combination strategy. For all time-domain features, the optimal frequency and time combination strategy showed the highest performance in distinguishing SF patients from NSF patients (area under the curve (AUC) = 0.790 ± 0.159). Furthermore, optimal performance was achieved by utilizing a feature vector derived from statistical parameters within the 39- to 41-Hz frequency band with a window length of 210 s, as evidenced by an AUC of 0.748. By identifying the optimal parameters, we improved the performance of the prediction model. These parameters can serve as standard parameters for predicting outcomes based on resting-state EEG signals.

PMID:38110465 | DOI:10.1038/s41598-023-49255-2

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Nevin Manimala Statistics

Lesion detection in women breast’s dynamic contrast-enhanced magnetic resonance imaging using deep learning

Sci Rep. 2023 Dec 18;13(1):22555. doi: 10.1038/s41598-023-48553-z.

ABSTRACT

Breast cancer is one of the most common cancers in women and the second foremost cause of cancer death in women after lung cancer. Recent technological advances in breast cancer treatment offer hope to millions of women in the world. Segmentation of the breast’s Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is one of the necessary tasks in the diagnosis and detection of breast cancer. Currently, a popular deep learning model, U-Net is extensively used in biomedical image segmentation. This article aims to advance the state of the art and conduct a more in-depth analysis with a focus on the use of various U-Net models in lesion detection in women’s breast DCE-MRI. In this article, we perform an empirical study of the effectiveness and efficiency of U-Net and its derived deep learning models including ResUNet, Dense UNet, DUNet, Attention U-Net, UNet++, MultiResUNet, RAUNet, Inception U-Net and U-Net GAN for lesion detection in breast DCE-MRI. All the models are applied to the benchmarked 100 Sagittal T2-Weighted fat-suppressed DCE-MRI slices of 20 patients and their performance is compared. Also, a comparative study has been conducted with V-Net, W-Net, and DeepLabV3+. Non-parametric statistical test Wilcoxon Signed Rank Test is used to analyze the significance of the quantitative results. Furthermore, Multi-Criteria Decision Analysis (MCDA) is used to evaluate overall performance focused on accuracy, precision, sensitivity, F[Formula: see text]-score, specificity, Geometric-Mean, DSC, and false-positive rate. The RAUNet segmentation model achieved a high accuracy of 99.76%, sensitivity of 85.04%, precision of 90.21%, and Dice Similarity Coefficient (DSC) of 85.04% whereas ResNet achieved 99.62% accuracy, 62.26% sensitivity, 99.56% precision, and 72.86% DSC. ResUNet is found to be the most effective model based on MCDA. On the other hand, U-Net GAN takes the least computational time to perform the segmentation task. Both quantitative and qualitative results demonstrate that the ResNet model performs better than other models in segmenting the images and lesion detection, though computational time in achieving the objectives varies.

PMID:38110462 | DOI:10.1038/s41598-023-48553-z

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Nevin Manimala Statistics

Improved exponential type ratio estimator in double sampling for stratification

Sci Rep. 2023 Dec 18;13(1):22520. doi: 10.1038/s41598-023-49772-0.

ABSTRACT

The objective of this research is to create a chain-ratio-type exponential estimator in order to estimate the finite population mean in double sampling for stratification. An estimator for population mean has been constructed based on the concept of chain-ratio estimators. The constructed estimator is compared to the standard unbiased estimator, as well as the other relevant existing estimators and conditions are shown to yield better results in terms of efficiency. To support the theoretical results the study has been done on both natural as well as simulated populations.

PMID:38110454 | DOI:10.1038/s41598-023-49772-0

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Assessing health-related quality of life of people with diabetes in Nigeria using the EQ-5D-5L: a cross-sectional study

Sci Rep. 2023 Dec 18;13(1):22536. doi: 10.1038/s41598-023-49322-8.

ABSTRACT

Assessing the health-related quality of life (HRQoL) of people with diabetes is important to evaluate treatment effectiveness and identify interventions that would be beneficial to the patients. This descriptive cross-sectional study aimed to assess the HRQoL of people with diabetes visiting 15 community pharmacies in Akwa Ibom State, Nigeria, and to identify its determinants. The English (Nigeria) version of the EQ-5D-5L was administered to 420 eligible patients between August and September 2021. Data were analyzed with SPSS (IBM version 25.0) and presented descriptively; differences in HRQoL scores were examined using inferential statistics. Statistical significance was set at p < 0.05. Most participants (56.8%) were female; 193 (49.6%) were between the ages of 30 and 49. The median (interquartile range, IQR) for the EQ VAS and EQ-5D-5L index scores, respectively, were 80.0 (65.0-85.0) and 0.77 (0.62-0.90). Most participants reported problems with usual activities (52.7%), pain/discomfort (60.2%), and anxiety/depression (57.6%). The EQ VAS score and EQ-5D-5L utility index were significantly (p < 0.05) associated with respondents’ age, marital status, work status, and personal monthly income. The HRQoL of participants was relatively high. Nevertheless, implementing strategies aimed at pain management and providing psychological support for people with diabetes in Nigeria may improve their HRQoL.

PMID:38110447 | DOI:10.1038/s41598-023-49322-8