Category: Statistics

Sci Rep. 2023 Feb 13;13(1):2513. doi: 10.1038/s41598-023-29841-0.

ABSTRACT

The limited electrical performance of microelectronic devices caused by low inter-particle connectivity and inferior printing quality is still the greatest hurdle to overcome for Aerosol jet printing (AJP) technology. Despite the incorporation of carbon nanotubes (CNTs) and specified solvents into functional inks can improve inter-particle connectivity and ink printability respectively, it is still challenging to consider multiple conflicting properties in mixture design simultaneously. This research proposes a novel hybrid multi-objective optimization method to determine the optimal functional ink composition to achieve low electrical resistivity and high printed line quality. In the proposed approach, silver ink, CNTs ink and ethanol are blended according to mixture design, and two response surface models (ReSMs) are developed based on the Analysis of Variance. Then a desirability function method is employed to identify a 2D optimal operating material window to balance the conflicting responses. Following that, the conflicting objectives are optimized in a more robust manner in the 3D mixture design space through the integration of a non-dominated sorting genetic algorithm III (NSGA-III) with the developed ReSMs and the corresponding statistical uncertainty. Experiments are conducted to validate the effectiveness of the proposed approach, which extends the methodology of designing materials with multi-component and multi-property in AJP technology.

PMID:36781965 | DOI:10.1038/s41598-023-29841-0

Sci Rep. 2023 Feb 13;13(1):2529. doi: 10.1038/s41598-023-29525-9.

ABSTRACT

Turbulence is a complex phenomenon that has a chaotic nature with multiple spatio-temporal scales, making predictions of turbulent flows a challenging topic. Nowadays, an abundance of high-fidelity databases can be generated by experimental measurements and numerical simulations, but obtaining such accurate data in full-scale applications is currently not possible. This motivates utilising deep learning on subsets of the available data to reduce the required cost of reconstructing the full flow in such full-scale applications. Here, we develop a generative-adversarial-network (GAN)-based model to reconstruct the three-dimensional velocity fields from flow data represented by a cross-plane of unpaired two-dimensional velocity observations. The model could successfully reconstruct the flow fields with accurate flow structures, statistics and spectra. The results indicate that our model can be successfully utilised for reconstructing three-dimensional flows from two-dimensional experimental measurements. Consequently, a remarkable reduction in the complexity of the experimental setup and the storage cost can be achieved.

PMID:36781944 | DOI:10.1038/s41598-023-29525-9

Sci Rep. 2023 Feb 13;13(1):2495. doi: 10.1038/s41598-023-27707-z.

ABSTRACT

Deceleration is considered a commonly practised means to assess Foetal Heart Rate (FHR) through visual inspection and interpretation of patterns in Cardiotocography (CTG). The precision of deceleration classification relies on the accurate estimation of corresponding event points (EP) from the FHR and the Uterine Contraction Pressure (UCP). This work proposes a deceleration classification pipeline by comparing four machine learning (ML) models, namely, Multilayer Perceptron (MLP), Random Forest (RF), Naïve Bayes (NB), and Simple Logistics Regression. Towards an automated classification of deceleration from EP using the pipeline, it systematically compares three approaches to create feature sets from the detected EP: (1) a novel fuzzy logic (FL)-based approach, (2) expert annotation by clinicians, and (3) calculated using National Institute of Child Health and Human Development guidelines. The classification results were validated using different popular statistical metrics, including receiver operating characteristic curve, intra-class correlation coefficient, Deming regression, and Bland-Altman Plot. The highest classification accuracy (97.94%) was obtained with MLP when the EP was annotated with the proposed FL approach compared to RF, which obtained 63.92% with the clinician-annotated EP. The results indicate that the FL annotated feature set is the optimal one for classifying deceleration from FHR.

PMID:36781920 | DOI:10.1038/s41598-023-27707-z

Sci Rep. 2023 Feb 13;13(1):2499. doi: 10.1038/s41598-023-29262-z.

ABSTRACT

Egypt is the country with the highest known hepatitis C virus (HCV) prevalence worldwide. The origin of gender differences in HCV prevalence is not usually well understood. This systematic review and meta-analysis aimed to review and evaluate the gender differences in HCV infection rates amongst Egyptians. Such data would be important to support prevention and control programs aiming to minimize HCV-related morbidity and mortality. PubMed, Scopus, and Web of Science (WOS) were searched for relevant articles published from 1st January 2011 to 13th December 2021, using the search terms (HCV OR “hepatitis C” OR hepacivirus) AND (prevalence OR seroprevalence OR epidemiology OR incidence OR magnitude). At first, retrieved articles were screened, and then relevant data were extracted and analyzed. Descriptive statistics were used for data analysis. Out of 616 studies from databases, only 30 were included after the full-text screening, with 193,621 included participants: 97,597 male and 96,024 female. The overall seroprevalence of HCV antibodies in all included studies was 0.02 (CI – 0.23 to 0.28), with no significant difference between males and females. However, HCV RNA positivity was significantly more prevalent in males than females in adults and the general population (after excluding high-risk groups). In children, no statistically significant differences between males and females were found in the seroprevalence of HCV antibodies nor in the prevalence of PCR positivity. HCV RNA positivity is significantly higher in males than females in adults, while there are no gender differences in children.

PMID:36781919 | DOI:10.1038/s41598-023-29262-z

Sci Rep. 2023 Feb 13;13(1):2520. doi: 10.1038/s41598-023-29282-9.

ABSTRACT

Impaired gut homeostasis is associated with stroke often presenting with leaky gut syndrome and increased gut, brain, and systemic inflammation that further exacerbates brain damage. We previously reported that intracisternal administration of Tanshinone IIA-loaded nanoparticles (Tan IIA-NPs) and transplantation of induced pluripotent stem cell-derived neural stem cells (iNSCs) led to enhanced neuroprotective and regenerative activity and improved recovery in a pig stroke model. We hypothesized that Tan IIA-NP + iNSC combination therapy-mediated stroke recovery may also have an impact on gut inflammation and integrity in the stroke pigs. Ischemic stroke was induced, and male Yucatan pigs received PBS + PBS (Control, n = 6) or Tan IIA-NP + iNSC (Treatment, n = 6) treatment. The Tan IIA-NP + iNSC treatment reduced expression of jejunal TNF-α, TNF-α receptor1, and phosphorylated IkBα while increasing the expression of jejunal occludin, claudin1, and ZO-1 at 12 weeks post-treatment (PT). Treated pigs had higher fecal short-chain fatty acid (SCFAs) levels than their counterparts throughout the study period, and fecal SCFAs levels were negatively correlated with jejunal inflammation. Interestingly, fecal SCFAs levels were also negatively correlated with brain lesion volume and midline shift at 12 weeks PT. Collectively, the anti-inflammatory and neuroregenerative treatment resulted in increased SCFAs levels, tight junction protein expression, and decreased inflammation in the gut.

PMID:36781906 | DOI:10.1038/s41598-023-29282-9

Sci Rep. 2023 Feb 13;13(1):2522. doi: 10.1038/s41598-023-28985-3.

ABSTRACT

We present a topological method for the detection and quantification of bone microstructure from non-linear microscopy images. Specifically, we analyse second harmonic generation (SHG) and two photon excited autofluorescence (TPaF) images of bone tissue which capture the distribution of matrix (fibrillar collagen) structure and autofluorescent molecules, respectively. Using persistent homology statistics with a signed Euclidean distance transform filtration on binary patches of images, we are able to quantify the number, size, distribution, and crowding of holes within and across samples imaged at the microscale. We apply our methodology to a previously characterized murine model of skeletal pathology whereby vascular endothelial growth factor expression was deleted in osteocalcin-expressing cells (OcnVEGFKO) presenting increased cortical porosity, compared to wild type (WT) littermate controls. We show significant differences in topological statistics between the OcnVEGFKO and WT groups and, when classifying the males, or females respectively, into OcnVEGFKO or WT groups, we obtain high prediction accuracies of 98.7% (74.2%) and 77.8% (65.8%) respectively for SHG (TPaF) images. The persistence statistics that we use are fully interpretable, can highlight regions of abnormality within an image and identify features at different spatial scales.

PMID:36781895 | DOI:10.1038/s41598-023-28985-3

Sci Rep. 2023 Feb 13;13(1):2551. doi: 10.1038/s41598-023-27465-y.

ABSTRACT

Several guidelines classify autologous stem cell transplantation (ASCT) as a low to intermediate risk group for infection. In a nationwide population-based study, using the Korean Health Insurance Review and Assessment Service database, patients with lymphoma and multiple myeloma (MM) who underwent ASCT from 2002 to 2016 were retrospectively analyzed. Cumulative incidence rates (CIRs) and risk factors of opportunistic infections were investigated. CIRs of fungal, Varicella zoster virus (VZV), cytomegalovirus (CMV), and Pneumocystis jirovecii infections in lymphoma were 7.9%, 16.0%, 7.4%, and 5.1%, respectively, and CIRs in MM were 6.3%, 19.1%, 4.2%, and 5.6%, respectively. Fungal infection was significantly higher in patients with previous infection (Hazard ratio (HR) 2.003, p = 0.005) in lymphoma. Incidence of CMV infection was significantly higher in patients with prior CMV infection: HR 4.920, p < 0.001 (lymphoma); HR 3.022, p = 0.030 (MM). VZV infection was significantly lower in patients receiving prophylaxis: HR 0.082, p < 0.001 (lymphoma); HR 0.096, p < 0.001 (MM). For P. jirovecii infection, busulfex and melphalan conditioning (HR 1.875, p = 0.032) and previous P. jirovecii infection (HR 4.810, p < 0.001) had a higher incidence in MM. Patients who underwent ASCT should receive VZV prophylaxis and prophylaxis for fungal and P. jirovecii may be considered in patients with previous same infection.

PMID:36781859 | DOI:10.1038/s41598-023-27465-y

Indian J Gastroenterol. 2023 Feb 13. doi: 10.1007/s12664-022-01311-x. Online ahead of print.

ABSTRACT

BACKGROUND: Esophageal dysmotility has been attributed to opioid use. The goal was to assess the differences in pre- and post-treatment timed-barium esophagram (TBE) barium heights at 1 and 5 minutes and symptomatic response to treatment in esophagogastric junction outflow obstruction (EGJOO) patients according to opioid use status.

METHODS: We performed a retrospective cohort study. Consecutive patients with EGJOO were eligible for inclusion. Data were collected on demographics, pre and post-treatment 1 and 5 minutes TBE barium heights and symptom outcomes. Groups were compared according to opioid use.

RESULTS: Thirty-one EGJOO patients met the inclusion criteria. All patients were treated with pneumatic dilation. Of the 31 patients, 11 (35%) had opioid exposure and 20 (65%) did not. The median follow-up post-treatment was two months (range 1-47 months). There was no statistically significant difference in post-treatment outcomes for opioid exposed vs. unexposed groups. The median per cent decrease in the TBE barium height at 1 minute was 100% for the opioid exposed vs. 71% for the unexposed group (p = 0.92). The median per cent decrease in the TBE barium height at 5 minutes was zero % for the opioid exposed and unexposed groups (p = 0.67). The incidence of symptomatic improvement was 82% (9/11) for the opioid exposed group vs 95% (19/20) for the unexposed group (p = 0.28).

CONCLUSIONS: Patients with EGJOO seem to respond to treatment similarly regardless of being on opioids.

PMID:36781814 | DOI:10.1007/s12664-022-01311-x

J Urban Health. 2023 Feb 13. doi: 10.1007/s11524-023-00718-x. Online ahead of print.

ABSTRACT

Breast cancer is the most commonly diagnosed cancer among women worldwide. Studies have reported minimal birth cohort effects on the incidence rates of breast cancer in Western countries but have reported notable birth cohort effects in some Asian countries. The risks of breast cancer may also vary within a country. In the present study, we abstracted female invasive breast cancer data from the Taiwan Cancer Registry for the period 1997-2016. We used the age-period-cohort model to compare birth cohort effects on breast cancer incidence rates between urban and rural regions in Taiwan. We identified a notable urban-rural disparity in birth cohort effects on breast cancer incidence rates in women in Taiwan. The incidence rates in the urban regions were higher than those in the rural regions across all cohorts. However, the incidence rates rose faster in the rural regions than in the urban regions across the cohorts. The risks of breast cancer observed for women born in 1992 were approximately 22 and 11 times than those observed for women born in 1917 in rural and urban regions, respectively. The observed gap in breast cancer incidence rates between the urban and rural regions gradually disappeared across the cohorts. Accordingly, we speculate that urbanization and westernization in Taiwan may be the drivers of female breast cancer incidence rates across birth cohorts.

PMID:36781812 | DOI:10.1007/s11524-023-00718-x

Methods Mol Biol. 2023;2628:365-391. doi: 10.1007/978-1-0716-2978-9_24.

ABSTRACT

Large-scale profiling of intact glycopeptides is critical but challenging in glycoproteomics. Data-independent acquisition (DIA) mass spectrometry is an emerging technology with deep proteome coverage as well as accurate quantitative capability for large-scale proteomics studies and has also been applied to the field of glycoproteomics. In this protocol, we describe how to analyze data from a DIA experiment for profiling serum intact N-glycopeptides. We present a comprehensive data analysis workflow using GproDIA, including glycopeptide spectral library building, chromatographic feature extraction from the DIA data, and feature scoring with appropriate statistical control of error rates. We anticipate that this method could provide a powerful tool to explore the serum glycoproteome.

PMID:36781798 | DOI:10.1007/978-1-0716-2978-9_24