Tag: pubmed

World J Urol. 2025 Aug 9;43(1):480. doi: 10.1007/s00345-025-05864-2.

ABSTRACT

PURPOSE: To assess the predictive value of quantitative parameters derived from conventional MRI for determining the pathological WHO/ISUP grade in patients with clear cell renal cell carcinoma (ccRCC) before surgery, and to construct a nomogram based on these parameters.

METHODS: This study analyzed ccRCC patients who underwent preoperative abdominal multi-sequence MRI, dynamic contrast-enhanced MRI, and nephrectomy at our hospital. Patients were pathologically classified into low-grade (WHO/ISUP 1/2) and high-grade (WHO/ISUP 3/4) groups. Information on clinical characteristics and quantitative MR parameters was collected. Multivariate logistic regression analyses were performed to create a nomogram incorporating the quantitative MR parameters with statistical significance to preoperatively predict the pathological grade of ccRCC. The area under the curve (AUC) was used to assess the nomogram’s predictive performance.

RESULTS: Binary univariate and multivariate logistic regression analyses identified maximum tumor diameter, ADC value, and corticomedullary enhancement as independent predictors of high-grade ccRCC. The quantitative MRI-based nomogram demonstrated high predictive performance, with an AUC of 0.936 (95% confidence interval [CI]: 0.901-0.972). What’s more, we found an ADC value of 1.47 × 10^-3mm²/s and a corticomedullary enhancement value of 0.90 were determined to be the optimal cut-off values, yielding the highest Youden index for differentiating between low-grade and high-grade ccRCC. The calibration curve and the Hosmer-Lemeshow test revealed that the predicted probability of the quantitative-MR nomogram had a good fitness (χ2 = 12.542, p = 0.129).

CONCLUSION: The quantitative MR-based nomogram demonstrated excellent performance in the preoperative prediction of pathological WHO/ISUP grade in ccRCC.

PMID:40782267 | DOI:10.1007/s00345-025-05864-2

Bull Math Biol. 2025 Aug 9;87(9):126. doi: 10.1007/s11538-025-01504-3.

ABSTRACT

Nonlocal perception plays a crucial role in studying animal cognitive movement modeling. In this paper, the impact of nonlocal perception on pattern formation is analyzed, and it is applied to study the control of pine wilt disease. It turns out that perceptual movement can provide a theoretical scientific basis for the multi-point outbreaks and spatiotemporal aggregation of pine wilt disease. For the top-hat kernel, we concentrate on the joint effect of perception scale and delay on the stability, and find that Turing-Hopf bifurcation occurs due to their interaction. Besides, the patterns near the bifurcation points are simulated in detail by adopting parameters with actual biological meaning, which are selected by analyzing real data, and diverse complicated spatiotemporal patterns are obtained, such as peak alternating periodic patterns and spatiotemporal aggregation patterns. Finally, we demonstrate that the artificial release of the parasitic natural enemy of the pest can drive the populations to reach stability in the form of the steady state or periodic solutions. The obtained results not only well explain the transmission mechanism of pine wilt disease, but also contribute to the study of biological phenomena such as the formations of flocks and swarms.

PMID:40782263 | DOI:10.1007/s11538-025-01504-3

Arch Toxicol. 2025 Aug 9. doi: 10.1007/s00204-025-04142-9. Online ahead of print.

NO ABSTRACT

PMID:40782261 | DOI:10.1007/s00204-025-04142-9

Clin Exp Med. 2025 Aug 9;25(1):284. doi: 10.1007/s10238-025-01734-8.

ABSTRACT

While machine learning (ML) approaches are commonly utilized in medical diagnostics, the accuracy of these methods in identifying psoriatic arthritis (PsA) remains uncertain. To evaluate the accuracy of ML approaches in the medical diagnosis of PsA. As a result, we thoroughly searched PubMed, Web of Science (WoS), Embase, Scopus, Cochrane Library, Wanfang, and the Chinese National Knowledge Infrastructure (CNKI) between their inception and October 1, 2024. The overall test performance of ML approaches was evaluated using the following metrics: pooled sensitivity, pooled specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), the area under the curve (AUC), and Fagan plot analysis. Additionally, we assessed the publication bias using the asymmetry test of the Deeks funnel plot. Six studies were included. The combined diagnostic data showed sensitivity of 0.72 (95% CI 0.60-0.81), specificity of 0.81 (95% CI 0.61-0.92), PLR of 4.00 (95% CI 3.06-5.23), NLR of 0.41 (95% CI 0.34-0.49), DOR of 11.06 (95% CI 6.42-19.06), and AUC of 0.81 (95% CI 0.78-0.84). The Fagan plot showed that the positive probability is 48% and the negative probability is 8%. Meta-regression identified country and sample size (all P < 0.05) as key sources of heterogeneity. The Deek funnel plot suggested that publication bias has no statistical significance (P = 0.99). The study suggests a promising accuracy of ML approaches in diagnosing PsA.

PMID:40782250 | DOI:10.1007/s10238-025-01734-8

High Blood Press Cardiovasc Prev. 2025 Aug 9. doi: 10.1007/s40292-025-00734-9. Online ahead of print.

ABSTRACT

INTRODUCTION: The Chinese visceral adiposity index (CVAI), a favorable surrogate index for assessing visceral fat distribution and function, has been proven to be associated with various conditions, including diabetes mellitus, cardiovascular diseases, and strokes. Nevertheless, evidence on the association of CVAI with the risk of incident hypertension among older adults is limited.

AIM: This study aimed to explore the association between CVAI and the risk of incident hypertension among older adults.

METHODS: Data were collected from the annual health examination dataset in Xinzheng, Henan Province from 2018 to 2023. A total of 10,353 participants aged ≥ 60 years were included. Cox proportional hazard models were used to examine the association between CVAI and the risk of incident hypertension by using hazard ratios (HRs) and 95% confidence intervals (CIs). Subgroup and sensitivity analyses were performed to confirm the association’s robustness. Additionally, the restricted cubic spline (RCS) was used to fit the dose-response association between CVAI and the risk of incident hypertension.

RESULTS: During a median of 2.72 years of follow-up, hypertension developed in 6990 participants. In the fully-adjusted model, compared with participants in the tertile 1 of CVAI, the tertile 3 (HR = 1.26, 95% CI: 1.19-1.34) of CVAI was associated with an increased risk of incident hypertension and per standard deviation (SD) increase was associated with a 12% (HR = 1.12, 95% CI: 1.09-1.15) increased risk of incident hypertension. Similar significant associations were observed in subgroup and sensitivity analyses. Additionally, the RCS analysis showed a significant dose-response association of CVAI with the risk of incident hypertension (P overall < 0.001 and P nonlinear = 0.238).

CONCLUSIONS: These results suggested a positive association between CVAI and the risk of incident hypertension among older adults.

PMID:40782245 | DOI:10.1007/s40292-025-00734-9

Jpn J Radiol. 2025 Aug 9. doi: 10.1007/s11604-025-01849-8. Online ahead of print.

ABSTRACT

PURPOSE: To assess the efficacy of super-resolution deep learning reconstruction (SR-DLR) with motion reduction algorithm (SR-DLR-M) in mitigating aorta motion artifacts compared to SR-DLR and deep learning reconstruction with motion reduction algorithm (DLR-M).

MATERIALS AND METHODS: This retrospective study included 86 patients (mean age, 65.0 ± 14.1 years; 53 males) who underwent contrast-enhanced CT including the chest region. CT images were reconstructed with SR-DLR-M, SR-DLR, and DLR-M. Circular or ovoid regions of interest were placed on the aorta, and the standard deviation of the CT attenuation was recorded as quantitative noise. From the CT attenuation profile along a line region of interest that intersected the left common carotid artery wall, edge rise slope and edge rise distance were calculated. Two readers assessed the images based on artifact, sharpness, noise, structure depiction, and diagnostic acceptability (for aortic dissection).

RESULTS: Quantitative noise was 7.4/5.4/8.3 Hounsfield unit (HU) in SR-DLR-M/SR-DLR/DLR-M. Significant differences were observed between SR-DLR-M vs. SR-DLR and DLR-M (p < 0.001). Edge rise slope and edge rise distance were 107.1/108.8/85.8 HU/mm and 1.6/1.5/2.0 mm, respectively, in SR-DLR-M/SR-DLR/DLR-M. Statistically significant differences were detected between SR-DLR-M vs. DLR-M (p ≤ 0.001 for both). Two readers scored artifacts in SR-DLR-M as significantly better than those in SR-DLR (p < 0.001). Scores for sharpness, noise, and structure depiction in SR-DLR-M were significantly better than those in DLR-M (p ≤ 0.005). Diagnostic acceptability in SR-DLR-M was significantly better than that in SR-DLR and DLR-M (p < 0.001).

CONCLUSIONS: SR-DLR-M provided significantly better CT images in diagnosing aortic dissection compared to SR-DLR and DLR-M.

PMID:40782239 | DOI:10.1007/s11604-025-01849-8

Ir J Med Sci. 2025 Aug 9. doi: 10.1007/s11845-025-04025-z. Online ahead of print.

ABSTRACT

INTRODUCTION: On 20 May 2024, the Irish government legalized e-scooter use on public roads under the Road Traffic and Roads Act 2023, requiring users to be over 16 years old and adhere to a 20 km/h speed limit. With the rising popularity of e-scooters, this study examines the impact of this legislation on the incidence, clinical presentation, and management of maxillofacial injuries.

AIMS: To compare the demographics, incidence rates, clinical presentation, injury patterns, and management of patients presenting to the National Maxillofacial Trauma Unit with e-scooter-related injuries before and after the legislative change.

METHODS: A retrospective cohort study at St James’s Hospital analysed two 9-month periods: pre-legislation (May 2023-Feb 2024) and post-legislation (May 2024-Feb 2025). All patients presenting with e-scooter-related maxillofacial injuries were included. Data collected encompassed demographics, risk factors, injury details, head and non-maxillofacial injuries, admission details (length of stay, time to treatment), treatment methods, and clinical outcomes. Statistical analysis compared the two periods.

RESULTS: The pre-legislation period included 22 patients with 26 injuries, while the post-legislation period had 28 patients with 36 injuries. E-scooter injuries increased from 1.7 to 2.3% of trauma presentations. Post-legislation, male patients increased from 59 to 71.4%, and non-Irish nationals from 41 to 46.4%. Injuries among Dublin residents rose from 45.5 to 75%. The mean age remained consistent (~ 33 years), and patients under 16 years decreased from 3 to 1. Helmet use dropped from 22.7 to 17.9%, while alcohol/substance involvement increased from 18.2 to 35.7%. Facial injuries rose from 26 to 36, with admission rates increasing from 31.2 to 35.7%. Surgical procedures increased from 9 to 13.

CONCLUSION: While the legislation may have reduced injuries among those under 16 and head trauma incidence, overall injury rates and surgical interventions continue to rise with growing e-scooter use. Ongoing surveillance and policy evaluation are essential for effective injury prevention strategies.

PMID:40782230 | DOI:10.1007/s11845-025-04025-z

Ir J Med Sci. 2025 Aug 9. doi: 10.1007/s11845-025-04003-5. Online ahead of print.

ABSTRACT

BACKGROUND: Lipoprotein(a) is a low-density lipoprotein-like particle covalently bound to apolipoprotein(a). It exhibits pro-atherogenic and pro-inflammatory effects and is an established independent monogenic determinant of atherosclerotic cardiovascular disease and aortic valve stenosis [1-4].

AIMS: To establish the Lp(a) distribution in a native Irish population and to explore if a certain lipid profile was associated with high Lp(a) level.

METHODS: We retrospectively included all subjects with Lp(a) results tested in our laboratory between January 2021 and December 2022. Patients were divided into Irish and non-Irish name subgroups [16]. We analysed the Lp(a) distribution across lipidaemic subgroups. Statistical analyses were completed in Jamovi programme V2.3.26.

RESULTS: In total 2762 patients of which 1899 had also a lipid profile. Eighty-five percent (n = 2359) of individuals had Irish surnames and 60% (n = 1419) were males. Mean age of all patients was 56 ± 17 years. The median lipoprotein(a) level was 34.5 nmol/L (interquartile interval < 20 to 153). The Lp(a) median in females was 37.3 (interquartile interval < 20 to 169) versus males 32.9 (interquartile interval < 20 to 147) (p = 0.029). A total of 62.9% (n = 1738) of Irish subjects had Lp(a) levels < 75 nmol/L, 7.56% of them (n = 209) between 75 and 125 nmol/L and 29.5% (n = 815) of subjects had Lp(a) > 125 nmol/L.

CONCLUSIONS: This is the largest study of Lp(a) distribution in an Irish population revealing positively skewed Lp(a) serum levels. This is not entirely reflective of the general population but brings to the fore the additional hidden high risks in those patients attending cardiovascular services. More education is needed to increase the use of Lp(a) measurements and guide further therapy.

PMID:40782229 | DOI:10.1007/s11845-025-04003-5

Eur Radiol. 2025 Aug 9. doi: 10.1007/s00330-025-11778-9. Online ahead of print.

ABSTRACT

OBJECTIVE: To evaluate the discriminative power of coronary artery calcium (CAC) score-based Cox models for predicting cardiovascular disease (CVD) in older adults with longstanding diabetes, a population at elevated CVD risk. We also aimed to determine whether adding computed tomography (CT)-derived costal cartilage calcification (CCC) improves risk prediction, given the potential limitation of CAC due to widespread soft tissue calcification.

MATERIALS AND METHODS: We analyzed adults ≥ 65 years from the multi-ethnic study of atherosclerosis with longstanding diabetes mellitus (DM, ≥ 5 years, n = 231) and compared them to non-DM participants (n = 1148). We evaluated CAC-based risk models (adjusted for Framingham Risk Score, race/ethnicity, and statin use) and assessed the impact of adding CCC on model performance using Cox proportional-hazards regression and Harrell’s C-statistic to predict CVD and coronary heart disease (CHD) incidence. CHD events included fatal coronary events, resuscitated cardiac arrest, myocardial infarction, adjudicated angina, and revascularization with angina. CVD events encompassed CHD, stroke (excluding transient ischemic attack), cardiovascular death, or other atherosclerotic deaths.

RESULTS: Over 8.7 years, CVD and CHD events occurred in 17% and 10% of DM participants and 11% and 5% of non-DM participants, respectively. In longstanding DM participants, doubling of CAC was associated with higher CVD risk (HR: 1.13; 95% CI: 1.01-1.26), with model discrimination improving from C-statistic 0.66 to 0.69 (p = 0.02) after adding CCC. For CHD, the corresponding HR was 1.05 (95% CI: 0.98-1.13), and the C-statistic rose from 0.65 to 0.69 (p = 0.04). In non-DM participants, CCC did not enhance model performance for either CVD or CHD (p > 0.5).

CONCLUSION: CCC, a measurable biomarker of non-cardiovascular calcification from any conventional CT, improves CVD and CHD risk prediction models’ performance in older adults with longstanding DM.

KEY POINTS: Question Coronary artery calcium (CAC) may have limited discriminative power for predicting cardiovascular outcomes in older adults with longstanding diabetes. Findings Costal cartilage calcification (CCC), a biomarker of non-cardiovascular calcification from CT, improves the prediction of cardiovascular disease and coronary heart disease risks in this population. Clinical relevance Incorporating CCC, which can be easily measured using existing CAC assessment tools on CT scans, into cardiovascular risk assessment could refine clinical decision-making and improve individualized risk stratification in older adults with longstanding diabetes.

PMID:40782222 | DOI:10.1007/s00330-025-11778-9

Eur Radiol. 2025 Aug 9. doi: 10.1007/s00330-025-11813-9. Online ahead of print.

ABSTRACT

OBJECTIVES: Cholesteatomas (CSs) are locally aggressive and can infiltrate adjacent structures as they expand. This study aimed to establish a straightforward scoring system using dual-layer detector dual-energy CT (DL-DECT) to improve the identification of cholesteatomas in patients.

MATERIALS AND METHODS: Between August 2023 and July 2024, 871 patients with soft tissue density shadows in the ear region who underwent DL-DECT examination were retrospectively enrolled at our institute. Surgical treatments followed by pathological examinations were performed. Based on pathological findings, the lesions were classified into cholesteatoma (CS) and non-cholesteatoma (non-CS) groups. The diagnostic performance of anatomical and quantitative parameters derived from DL-DECT was evaluated using receiver operating characteristic (ROC) curve analysis. Logistic regression was applied to develop a diagnostic scoring system.

RESULTS: A total of 87 patients (median age, 51 years; 45 men and 42 women) with suspected temporal bone CS were included, comprising 44 CS cases and 43 non-CS cases. The effective atomic number (Zeff) demonstrated the highest diagnostic accuracy (area under the curve [AUC] = 0.786), followed by the slope of the energy spectral curve (AUC = 0.784), scutum destruction (AUC = 0.759), and CT40 keV (AUC = 0.724). Logistic regression identified two significant predictors, which were incorporated into the scoring system. When the system score reached 2 points (Zeff ≤ 7.12 accompanied by scutum destruction), the AUC in the ROC analysis reached 0.868 (95% confidence interval: 0.778-0.931), significantly outperforming each individual parameter (all p < 0.05).

CONCLUSION: The DL-DECT-derived scoring system serves as an innovative imaging marker for detecting CSs.

KEY POINTS: Question Accurate differentiation between CSs and non-CSs is critical for selecting surgical approaches. However, high-resolution CT demonstrates limited discriminatory power. Findings A straightforward diagnostic scoring system incorporating Zeff ≤ 7.12 and scutum destruction was developed to efficiently identify patients with CS. Clinical relevance This scoring system may facilitate the early identification of CS, potentially improving patient outcomes through timely intervention.

PMID:40782221 | DOI:10.1007/s00330-025-11813-9