Tag: pubmed

Pediatr Res. 2024 May 11. doi: 10.1038/s41390-024-03232-1. Online ahead of print.

ABSTRACT

BACKGROUND: To investigate association of prenatal risk factors and neonatal outcomes of preterm infants with pulmonary hypertension (PH).

METHODS: A prospective cohort study of very-low-birth-weight infants born at 22-29 weeks’ gestation who received PH-specific treatment during hospitalization. Infants were classified using a two-step cluster analysis based on gestational age (GA), small-for-gestational-age (SGA), exposure to antenatal corticosteroids (ACS), histologic chorioamnionitis (HCA), and oligohydramnios.

RESULTS: Among 910 infants, six clusters were identified: cluster A (HCA, n = 240), cluster B (oligohydramnios, n = 79), cluster C (SGA, n = 74), cluster D (no-ACS, n = 109), cluster E (no dominant parameter, n = 287), and cluster F (HCA and oligohydroamnios, n = 121). Cluster A was used as a reference group for comparisons among clusters. Compared to cluster A, cluster C (aHR: 1.63 [95% CI: 1.17-2.26]) had higher risk of overall in-hospital mortality. Clusters B (aHR: 1.52 [95% CI: 1.09-2.11]), D (aHR: 1.71 [95% CI: 1.28-2.30]), and F (aHR: 1.51 [95% CI: 1.12-2.03]) had higher risks of receiving PH-specific treatment within the first week of birth compared to cluster A.

CONCLUSION: These findings may provide a better understanding of prenatal risk factors contributing to the development of PH.

IMPACT: Pulmonary hypertension (PH), presenting as hypoxic respiratory failure, has complex etiologies in preterm infants. Although multifactorial risks for the development of PH in preterm infants are known, few studies have classified infants with similar etiologies for PH. Each cluster has distinct patterns of prenatal condition and neonatal outcome.

PMID:38734814 | DOI:10.1038/s41390-024-03232-1

Arch Dermatol Res. 2024 May 11;316(5):161. doi: 10.1007/s00403-024-02921-5.

NO ABSTRACT

PMID:38734810 | DOI:10.1007/s00403-024-02921-5

Sci Rep. 2024 May 11;14(1):10813. doi: 10.1038/s41598-024-61504-6.

ABSTRACT

To evaluate the development of neutralizing Anti-Spike Protein IgG (Anti-S-IgG) during twin pregnancies before conception vs. during pregnancy. In this prospective study, three blood samples were collected from pregnant women and subjected to anti-S-IgG immunodiagnostics. The patient’s medical records, including vaccination and PCR test results, were collected from the hospital’s electronic database. Age-matched non-pregnant women were used as a control group. We enrolled 83 women with twin pregnancies. 49 women were vaccinated before conception, 21 women were vaccinated during pregnancy, and 13 were not vaccinated. Of the 13 women who weren’t vaccinated, three became positive during pregnancy, and all three were severely ill. By contrast, in women who were vaccinated during or before pregnancy, COVID-19 infection during pregnancy caused only mild symptoms. A ten-fold lower level of neutralizing Anti-S-IgG in the 3rd trimester was observed in healthy women who were vaccinated before conception and remained healthy until discharge from the hospital after delivery 1605 (IQR: 763-2410) compared to the healthy women who were vaccinated during pregnancy 152 AU/mL (IQR: 54-360). This difference was higher among women who were infected by COVID-19 (as verified by a positive PCR test). The third-trimester level of neutralizing Ant-S-IgG in the infected group was 4770 AU/mL (4760-6100) in infected women vaccinated before conception compared to those vaccinated during pregnancy who had 70 AU/mL (IQR: 20-170) (p < 0.001). In women vaccinated at 13-16 weeks gestation, neutralizing Anti-S-IgG at 20-22 weeks went up to 372 AU/mL (IQR: 120-1598) but rapidly dropped to 112 AU/mL (IQR: 54-357) at 28-30 weeks, (p < 0.001), a faster decline than in women vaccinated at a median 22 weeks before conception. Being infected by COVID-19 before conception was linked to having low Anti-S-IgG levels during pregnancy, whereas being infected by COVID-19 during pregnancy led to a very high response in the 3rd trimester. In twin pregnancies, significantly lower neutralizing Anti-S-IgG levels were observed in women vaccinated during pregnancy compared to those vaccinated before conception, whether infected or not infected by COVID-19. A full course of vaccination before conception is recommended.Trial registration. ClinicalTrials.gov Protocol Registration and Results System (PRS) Receipt Release Date: October 4, 2021. https://clinicaltrials.gov/ ID: NCT04595214.

PMID:38734805 | DOI:10.1038/s41598-024-61504-6

Arch Dermatol Res. 2024 May 11;316(5):151. doi: 10.1007/s00403-024-02858-9.

NO ABSTRACT

PMID:38734798 | DOI:10.1007/s00403-024-02858-9

Abdom Radiol (NY). 2024 May 11. doi: 10.1007/s00261-024-04349-x. Online ahead of print.

ABSTRACT

BACKGROUND: Seminal vesicle involvement (SVI) in patients with newly diagnosed prostate cancer is associated with high rates of treatment failure and tumor recurrence; correct identification of SVI allows for effective management decisions and surgical planning.

METHODS: This single-center retrospective study analyzed MR images of the seminal vesicles from patients undergoing radical prostatectomy with confirmed T3b disease, comparing them to a control group without SVI matched for age and Gleason grade with a final stage of T2 or T3a. Seminal vesicles were segmented by an experienced uroradiologist, “raw” and bladder-normalized T2 signal intensity, as well as SV volume, were obtained.

RESULTS: Among the 82 patients with SVI, 34 (41.6%) had unilateral invasion, and 48 (58.4%) had bilateral disease. There was no statistically significant difference in the degree of distension between normal and involved seminal vesicles (P = 0.08). Similarly, no statistically significant difference was identified in the raw SV T2 signal intensity (P = 0.09) between the groups. In the 159 patients analyzed, SVI was prospectively suspected in 10 of 82 patients (specificity, 100%; sensitivity, 12.2%). In all these cases, lesions macroscopically invaded the seminal vesicle, and the raw T2 signal intensity was significantly lower than that in the SVI and control groups (P = 0.02 and 0.01).

CONCLUSION: While signal intensity measurements in T2-weighted images may provide insight into T3b disease, our findings suggest that this data alone is insufficient to reliably predict SVI, indicating the need for further investigation and complementary diagnostic approaches.

PMID:38734785 | DOI:10.1007/s00261-024-04349-x

Arch Dermatol Res. 2024 May 11;316(5):160. doi: 10.1007/s00403-024-02883-8.

NO ABSTRACT

PMID:38734784 | DOI:10.1007/s00403-024-02883-8

Arch Dermatol Res. 2024 May 11;316(5):165. doi: 10.1007/s00403-024-02922-4.

NO ABSTRACT

PMID:38734782 | DOI:10.1007/s00403-024-02922-4

Sci Rep. 2024 May 11;14(1):10810. doi: 10.1038/s41598-024-61659-2.

ABSTRACT

In this study, we have presented a novel probabilistic model called the neutrosophic Burr-III distribution, designed for applications in neutrosophic surface analysis. Neutrosophic analysis allows for the incorporation of vague and imprecise information, reflecting the reality that many real-world problems involve ambiguous data. This ability to handle vagueness can lead to more robust and realistic models especially in situation where classical models fall short. We have also explored the neutrosophic Burr-III distribution in order to deal with the ambiguity and vagueness in the data where the classical Burr-III distribution falls short. This distribution offers valuable insights into various reliability properties, moment expressions, order statistics, and entropy measures, making it a versatile tool for analyzing complex data. To assess the practical relevance of our proposed distribution, we applied it to real-world data sets and compared its performance against the classical Burr-III distribution. The findings revealed that the neutrosophic Burr-III distribution outperformed than the classical Burr-III distribution in capturing the underlying data characteristics, highlighting its potential as a superior modeling toolin various fields.

PMID:38734768 | DOI:10.1038/s41598-024-61659-2

Sci Rep. 2024 May 11;14(1):10792. doi: 10.1038/s41598-024-61338-2.

ABSTRACT

Epilepsy is a chronic neurological disease, characterized by spontaneous, unprovoked, recurrent seizures that may lead to long-term disability and premature death. Despite significant efforts made to improve epilepsy detection clinically and pre-clinically, the pervasive presence of noise in EEG signals continues to pose substantial challenges to their effective application. In addition, discriminant features for epilepsy detection have not been investigated yet. The objective of this study is to develop a hybrid model for epilepsy detection from noisy and fragmented EEG signals. We hypothesized that a hybrid model could surpass existing single models in epilepsy detection. Our approach involves manual noise rejection and a novel statistical channel selection technique to detect epilepsy even from noisy EEG signals. Our proposed Base-2-Meta stacking classifier achieved notable accuracy (0.98 ± 0.05), precision (0.98 ± 0.07), recall (0.98 ± 0.05), and F1 score (0.98 ± 0.04) even with noisy 5-s segmented EEG signals. Application of our approach to the specific problem like detection of epilepsy from noisy and fragmented EEG data reveals a performance that is not only superior to others, but also is translationally relevant, highlighting its potential application in a clinic setting, where EEG signals are often noisy or scanty. Our proposed metric DF-A (Discriminant feature-accuracy), for the first time, identified the most discriminant feature with models that give A accuracy or above (A = 95 used in this study). This groundbreaking approach allows for detecting discriminant features and can be used as potential electrographic biomarkers in epilepsy detection research. Moreover, our study introduces innovative insights into the understanding of these features, epilepsy detection, and cross-validation, markedly improving epilepsy detection in ways previously unavailable.

PMID:38734752 | DOI:10.1038/s41598-024-61338-2

Sci Rep. 2024 May 11;14(1):10816. doi: 10.1038/s41598-024-61382-y.

ABSTRACT

r, s, t-spherical fuzzy (r, s, t-SPF) sets provide a robust framework for managing uncertainties in decision-making, surpassing other fuzzy sets in their ability to accommodate diverse uncertainties through the incorporation of flexible parameters r, s, and t. Considering these characteristics, this article explores sine trigonometric laws to enhance the applicability and theoretical foundation for r, s, t-SPF setting. Following these laws, several aggregation operators (AOs) are designed for aggregation of the r, s, t-SPF data. Meanwhile, the desired characteristics and relationships of these operators are studied under sine trigonometric functions. Furthermore, we build a group decision-making algorithm for addressing multiple attribute group decision-making (MAGDM) problems using the developed AOs. To exemplify the applicability of the proposed algorithm, we address a practical example regarding laptop selection. Finally, parameter analysis and a comprehensive comparison with existing operators are conducted to uncover the superiority and validity of the presented AOs.

PMID:38734743 | DOI:10.1038/s41598-024-61382-y