Tag: statistics

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 RMSE_SARIMA < RMSE_Prophet). 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

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

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

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

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

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

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

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

Sci Rep. 2023 Dec 18;13(1):22470. doi: 10.1038/s41598-023-48860-5.

ABSTRACT

A drop in physical activity and a deterioration in the capacity to undertake daily life activities are both connected with ageing and have negative effects on physical and mental health. An Elderly and Visually Impaired Human Activity Monitoring (EV-HAM) system that keeps tabs on a person’s routine and steps in if a change in behaviour or a crisis might greatly help an elderly person or a visually impaired. These individuals may find greater freedom with the help of an EVHAM system. As the backbone of human-centric applications like actively supported living and in-home monitoring for the elderly and visually impaired, an EVHAM system is essential. Big data-driven product design is flourishing in this age of 5G and the IoT. Recent advancements in processing power and software architectures have also contributed to the emergence and development of artificial intelligence (AI). In this context, the digital twin has emerged as a state-of-the-art technology that bridges the gap between the real and virtual worlds by evaluating data from several sensors using artificial intelligence algorithms. Although promising findings have been reported by Wi-Fi-based human activity identification techniques so far, their effectiveness is vulnerable to environmental variations. Using the environment-independent fingerprints generated from the Wi-Fi channel state information (CSI), we introduce Wi-Sense. This human activity identification system employs a Deep Hybrid convolutional neural network (DHCNN). The proposed system begins by collecting the CSI with a regular Wi-Fi Network Interface Controller. Wi-Sense uses the CSI ratio technique to lessen the effect of noise and the phase offset. The t- Distributed Stochastic Neighbor Embedding (t-SNE) is used to eliminate unnecessary data further. The data dimension is decreased, and the negative effects on the environment are eliminated in this process. The resulting spectrogram of the processed data exposes the activity’s micro-Doppler fingerprints as a function of both time and location. These spectrograms are put to use in the training of a DHCNN. Based on our findings, EVHAM can accurately identify these actions 99% of the time.

PMID:38110422 | DOI:10.1038/s41598-023-48860-5

Sci Data. 2023 Dec 18;10(1):909. doi: 10.1038/s41597-023-02814-8.

ABSTRACT

Retrieval-based Clinical Decision Support (ReCDS) can aid clinical workflow by providing relevant literature and similar patients for a given patient. However, the development of ReCDS systems has been severely obstructed by the lack of diverse patient collections and publicly available large-scale patient-level annotation datasets. In this paper, we collect a novel dataset of patient summaries and relations called PMC-Patients to benchmark two ReCDS tasks: Patient-to-Article Retrieval (ReCDS-PAR) and Patient-to-Patient Retrieval (ReCDS-PPR). Specifically, we extract patient summaries from PubMed Central articles using simple heuristics and utilize the PubMed citation graph to define patient-article relevance and patient-patient similarity. PMC-Patients contains 167k patient summaries with 3.1 M patient-article relevance annotations and 293k patient-patient similarity annotations, which is the largest-scale resource for ReCDS and also one of the largest patient collections. Human evaluation and analysis show that PMC-Patients is a diverse dataset with high-quality annotations. We also implement and evaluate several ReCDS systems on the PMC-Patients benchmarks to show its challenges and conduct several case studies to show the clinical utility of PMC-Patients.

PMID:38110415 | DOI:10.1038/s41597-023-02814-8