Tag: pubmed

Clin Rheumatol. 2024 Nov 25. doi: 10.1007/s10067-024-07244-4. Online ahead of print.

ABSTRACT

OBJECTIVE: To develop a simplified predictive model for identifying psoriatic arthritis (PsA) in psoriasis patients.

METHODS: Data from the National Health and Nutrition Examination Survey (NHANES) database were analyzed, including patients with psoriasis without arthritis (PsC) or PsA. The least absolute shrinkage and selection operator, Boruta algorithm, random forest, and stepwise regression were employed to select key variables from 38 potential predictors. Logistic regression models were constructed for each combination of selected variables and evaluated using receiver operating characteristic (ROC) curves, precision-recall (PR) curves, calibration plots, Brier scores, and decision curve analysis (DCA).

RESULTS: The study included 587 patients with psoriasis, 238 of whom had PsA. The variable combinations proposed by the Boruta algorithm exhibited the best overall performance. Key predictors in the Borutamodel included age, fasting glucose, education level, thyroid disease, hypertension, and chronic bronchitis. This model achieved area under the curve (AUC) of 0.781 (95% CI, 0.737-0.826) for the training set and 0.780 (95% CI, 0.712-0.848) for the testing set in the ROC curve analyses. The AUC values in the PR curves were 0.687 (95% CI, 0.611-0.757) and 0.653 (95% CI, 0.535-0.770), respectively. The Brier scores of 0.186 and 0.191 for the testing and training sets indicated a good fit, further supported by the calibration curves. DCA showed a net clinical benefit for decision thresholds ranging from 0.2 to 0.8 in both datasets.

CONCLUSION: The Borutamodel represents a promising tool for early risk assessment of PsA. Key Points • National Database Utilization: This study leverages the NHANES database to predict psoriatic arthritis risk, addressing previous limitations tied to regional or ethnic constraints. • Comprehensive Variable Analyses: The research examines 38 variables, including demographics, health conditions, laboratory results, and lifestyle factors, using four distinct screening methods and thorough evaluations of model performance. • Innovative Risk Model: The study introduces a novel risk assessment model that integrates age, fasting glucose, education, and comorbidities including hypertension, thyroid disease, and chronic bronchitis, thus moving beyond traditional focus on skin lesions and joint symptoms.

PMID:39585569 | DOI:10.1007/s10067-024-07244-4

Environ Monit Assess. 2024 Nov 25;196(12):1248. doi: 10.1007/s10661-024-13444-x.

ABSTRACT

Temperature accelerates the deterioration processes affecting grotto temples. As such, studies of the temperature distribution characteristics of grotto temples can provide an important basis for their protection. In this paper, the hourly surface temperatures of 123 grotto temples in China were studied using ERA5-Land hourly data from 1981 to 2020, obtained through the AI Earth platform. Using the local Python development environment, the daily surface temperature difference and highest and lowest temperatures of grotto temples were linearly fitted for each year, after which the monthly average temperature difference distribution was statistically analyzed to determine trends in temperature change. Then, the GIS Spatially Constrained Multivariate Clustering method was used to cluster the surface temperature characteristics. The results showed that the grotto temples in China can be mainly divided into seven regions, namely Xinjiang, Qinghai-Xizang Plateau, Hexi, Longdong, Shaanxi and North China, Southwest, and East and Southeast. The highest average surface temperature, greater than 15 °C, occurred in South China, and the lowest, close to 0 °C, occurred in the Qinghai-Xizang Plateau. The average surface temperature of the seven regions identified showed an increasing trend. The Qinghai-Xizang Plateau was affected by severe temperature differences throughout the year, with annual average daily temperature differences approaching 30 °C, followed by Xinjiang and Hexi region, with a perennial temperature difference of approximately 25 °C. The Longdong, Shaanxi, and North China regions had annual average daily temperature differences of 15-20 °C, whereas values for the South China region were less than 15 °C. The daily surface temperature differences of grotto temples reached their maximum values in April to May and their minimum values in December to January. All studied regions are subject to temperature-induced challenges: Xinjiang region faces particularly severe high-temperature influences, with a mean daily surface temperature of almost 45 °C in summer, followed by Hexi region with 35 °C or above, and the other regions with approximately 30 °C. The Qinghai-Xizang Plateau exhibits perennially low temperatures, with a mean daily minimum temperature below – 25 °C in winter; less than – 10 °C in the Xinjiang, Hexi, Longdong, Shaanxi, and North China regions; and approximately 0 °C in southern China. The relative impacts of temperature on grotto temples in each region are as follows: Xinjiang and Hexi > Qinghai-Xizang Plateau > Longdong, Shaanxi, and North China > Southwest China > East and Southeast China. This study has revealed the characteristics of surface temperature distribution in grotto temples in China and proposes appropriate protection measures, which will help improve national-scale practical mitigation of the threats facing these important cultural heritage sites.

PMID:39585564 | DOI:10.1007/s10661-024-13444-x

Jpn J Radiol. 2024 Nov 25. doi: 10.1007/s11604-024-01710-4. Online ahead of print.

ABSTRACT

PURPOSE: So far, there is no gold standard to diagnosis dysthyroid optic neuropathy (DON). Diffusion kurtosis imaging (DKI) has the potential to provide imaging biomarkers for the timely and accurate diagnosis of DON. This study aimed to explore the white matter (WM) alterations in thyroid-associated ophthalmopathy (TAO) patients with and without DON using DKI with tract-based spatial statistics method.

MATERIALS AND METHODS: Fifty-three TAO patients (21 DON and 32 non-DON) and 30 healthy controls (HCs) were recruited in this cross-sectional study. DKI data were analyzed and compared among groups. The correlations between diffusion parameters and clinical variables were assessed. Receiver-operating characteristic curve analysis was used to evaluate the feasibility of using DKI parameters to distinguish DON and non-DON.

RESULTS: Compared with HCs, both DON and non-DON groups exhibited significantly decreased radial kurtosis (RK), mean kurtosis (MK), axial kurtosis (AK), kurtosis fractional anisotropy, and fractional anisotropy values in several WM tracts. No significant differences were observed in mean diffusivity values among groups. Meanwhile, DON patients exhibited lower RK, MK, and AK values than non-DON patients mainly in the visual system. Significant correlations were observed between RK values of posterior thalamic radiation (PTR) and best-corrected visual acuity. For distinguishing DON, the RK values of PTR exhibited decent diagnostic performance.

CONCLUSION: Microstructural abnormalities in WM, especially in the visual system, could provide novel insights into the potential neural mechanisms of the disease, thereby contributing to the timely diagnosis of DON and the development of neuroprotective therapy.

PMID:39585557 | DOI:10.1007/s11604-024-01710-4

3D Print Med. 2024 Nov 25;10(1):39. doi: 10.1186/s41205-024-00245-8.

ABSTRACT

AIM: This study compares the precision, accuracy, and user experience of 3D body surface scanning of human subjects using the Artec Leo hand-held scanner and the iPad Pro as 3D scanning devices for capturing cervical and craniofacial data. The investigation includes assessing methods for correcting ‘dropped head syndrome’ during scanning, to demonstrate the ability of the scanner to be used to reconstruct body surface of patients.

METHODS: Eighteen volunteers with no prior history of neck weakness were scanned three times in three different positions, using the two different devices. Surface area, scanning time, and participant comfort scores were evaluated for both devices. Precision and accuracy were assessed using Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE), and Intra-Class Correlation Coefficients (ICC).

RESULTS: Surface area comparisons revealed no significant differences between devices and positions. Scanning times showed no significant difference between devices or positions. Comfort scores varied across positions. MAD analysis identified chin to chest measurements as having the highest variance, especially in scanning position 3. However, no statistical differences were found. MAPE results confirmed accuracy below 5% error for both devices. ICC scores indicated good reliability for both measurement methods, particularly for chin to chest measurements in positions 1 and 3.

CONCLUSION: The iPad Pro using the Qlone app demonstrates a viable alternative to the Artec Leo, particularly for capturing head and neck surface area within a clinical setting. The scanning resolution, with an error margin within ±5%, is consistent with clinically accepted standards for orthosis design, where padding and final fit adjustments allow for bespoke devices that accommodate patient comfort. This study highlights the comparative performance of the iPad, as well as suggests two methods which can be used within clinics to correct head drop for scanning.

PMID:39585546 | DOI:10.1186/s41205-024-00245-8

Bull Math Biol. 2024 Nov 25;87(1):4. doi: 10.1007/s11538-024-01376-z.

ABSTRACT

Single cell RNA-seq (scRNAseq) workflows typically start with a count matrix and end with the clustering of sampled cells. While a range of methods have been developed to cluster scRNAseq datasets, no theoretical tools exist to explain why a particular cluster exists or why a hypothesized cluster is missing. Recently, several authors have shown that eigenvalues of scRNAseq count matrices can be approximated using random matrix models. In this work, we extend these previous works to the study of a scRNAseq workflow. We model scaled count matrices using random matrices with normally distributed entries. Using these random matrix models, we quantify the differential expression of a cluster and develop predictions for the workflow, and in particular clustering, as a function of the differential expression. We also use results from random matrix theory (RMT) to develop predictive formulas for portions of the scRNAseq workflow. Using simulated and real datasets, we show that our predictions are accurate if certain conditions hold on differential expression, with our RMT based predictions requiring particularly stringent condition. We find that real datasets violate these conditions, leading to bias in our predictions, but our predictions are better than a naive estimator and we point out future work that can improve the predictions. To our knowledge, our formulas represents the first predictive results for scRNAseq workflows.

PMID:39585539 | DOI:10.1007/s11538-024-01376-z

Sci Rep. 2024 Nov 24;14(1):29096. doi: 10.1038/s41598-024-80190-y.

ABSTRACT

Often linked with the risk of various diseases, blood low-density lipoprotein cholesterol (LDL-C) levels are typically deemed more favorable when lower. The objective of this investigation is to elucidate the link between blood LDL-C levels and the risk of prostate cancer (PCa) in middle-aged and older men without hypertension in the United States. Utilizing continuous data from the National Health and Nutrition Examination Survey (NHANES) database spanning 2003-2010, a selection of 1,223 non-hypertensive men aged ≥ 40 years was made from a pool of 41,156 participants, ensuring no missing information. Regression analyses were employed to investigate the correlation between blood LDL-C levels and the PCa risk, while identifying potential inflection points indicative of threshold effects. Additionally, we scrutinized the linkage between cholesterol-lowering prescription drug usage and PCa. In our study of 2,224 participants, we found no significant correlation between blood LDL-C levels and the PCa risk after adjusting for confounding variables (Odds Ratio = 0.99; P-value > 0.05). However, upon conducting a subgroup analysis, we discovered a meaningful correlation between lower blood LDL-C levels and an increased PCa risk in the non-hypertensive population (Odds Ratio = 0.99; P-value < 0.05). Meanwhile, we identified a threshold effect and a tipping point at an LDL-C levels of 67 mg/dl. Furthermore, a significant correlation was identified between cholesterol-lowering prescription drug usage and a heightened PCa risk in the non-hypertensive population (Odds Ratio = 18.87; P-value < 0.05; P for interaction < 0.05). Our results indicate that in non-hypertensive middle-aged and older men residing in the United States, lower blood LDL-C levels are not necessarily better and the PCa risk escalates when blood LDL-C levels drop below 67 mg/dl, which may guide early screening and prognosis of PCa in specific populations. This finding calls for further validation via larger sample sizes and a more in-depth analysis of PCa history.

PMID:39582086 | DOI:10.1038/s41598-024-80190-y

Acta Ophthalmol. 2024 Nov 24. doi: 10.1111/aos.16785. Online ahead of print.

ABSTRACT

PURPOSE: To compare patient-reported visual function outcomes of immediate sequential bilateral cataract surgery (ISBCS) and delayed sequential bilateral cataract surgery (DSBCS).

METHODS: Single-center, randomised controlled trial of patients eligible for bilateral cataract surgery allocated to ISBCS or DSBCS. Patients filled out the Catquest-7SF questionnaire before surgery, 1 week after surgery, and 3 months after surgery.

RESULTS: Ninety-eight patients were included for analysis (ISBCS = 51; DSBCS = 47). In both groups, there was a statistically significant improvement in Catquest-7SF patient-reported outcomes after surgery (p < 0.001), and no difference between the ISBCS and DSBCS groups (p ≥ 0.424). At both 1 week and 3 months post-surgery, a statistically significantly higher proportion of patients were “very satisfied” with the surgical approach in the ISBCS group (94.1% at both 1 week and 3 months) compared to the DSBCS group (55.3% at 1 week and 63.8% at 3 months), both p < 0.001.

CONCLUSIONS: Both ISBCS and DSBCS are effective options to treat bilateral cataracts with no statistically significant difference in patient-reported vision outcomes. However, we found postoperative satisfaction with the surgical approach to be higher among ISBCS patients, which suggests that ISBCS-related benefits, such as fewer health care visits and shorter vision rehabilitation, are compelling to patients.

PMID:39582084 | DOI:10.1111/aos.16785

Sci Rep. 2024 Nov 24;14(1):29099. doi: 10.1038/s41598-024-80705-7.

ABSTRACT

Until recently, the main diagnostic methods for bladder cancer (BC) are still voided urine cytology and cystoscopy, and many drawbacks persist. In this retrospective study, we evaluated the sensitivity and specificity of the CellDetect assay in the detection of BC with comparison to standard diagnostic methods. Between August 2020 and July 2022, B-ultrasonography or computed tomography (CT) scan was performed for patients with hematuria or irritative voiding symptoms. If no bladder mass was detected, the patient was excluded. A total of 148 patients with bladder mass formed the final study cohort. The patients’ urine samples were measured with CellDetect assay, followed by cystoscopy or diagnostic transurethral resection of bladder tumor. The patients were divided into two groups based on previous history of BC: group P and group R. The analysis included descriptive statistics and percentages. Finally, 115 cases had a positive CellDetect result, with 68 cases in group P and 47 in group R, respectively. And 134 cases revealed malignant tumor pathologically. The overall sensitivity and specificity for all patients were 82.1% and 64.2%, respectively. Concerning the subgroups, the respective sensitivity and specificity were: in group P- 81.0% and 50.0%; and in group R- 85.2% and 83.3%, respectively. In conclusion, CellDetect assay demonstrated significant performance for diagnosis of BC: it can identify BC patients at early stage with significant diagnostic performance and good reliability. This assay might develop novel methods and ideas for future clinical practice.

PMID:39582079 | DOI:10.1038/s41598-024-80705-7

Npj Ment Health Res. 2024 Nov 24;3(1):57. doi: 10.1038/s44184-024-00101-x.

ABSTRACT

This study examines how pandemic-related worries affected mental health in India’s adults from 2020 to 2022. Using data from the Global COVID-19 Trends and Impact Survey (N = 2,576,174), it explores the associations between worry variables (financial stress, food insecurity, and COVID-19-related health worries) and self-reported symptoms of depression and anxiety. Our analysis, based on complete cases (N = 747,996), used survey-weighted models, adjusting for demographics and calendar time. The study finds significant associations between these worries and mental health outcomes, with financial stress being the most significant factor affecting both depression (adjusted odds ratio, aOR: 2.36; 95% confidence interval, CI: [2.27, 2.46]) and anxiety (aOR: 1.91; 95% CI: [1.81, 2.01])). Models with interaction terms revealed gender, residential status, and calendar time as effect modifiers. This study demonstrates that social media platforms like Facebook can effectively gather large-scale survey data to track mental health trends during public health crises.

PMID:39582077 | DOI:10.1038/s44184-024-00101-x

Sci Rep. 2024 Nov 24;14(1):29118. doi: 10.1038/s41598-024-80697-4.

ABSTRACT

Spinal stenosis is a commonly chronic spinal degenerative disease, which is a major cause of pain and dysfunction in the elderly. Mendelian randomization (MR) has been widely applied to repurpose licensed drugs and identify novel therapeutic targets. Consequently, we intended to identify new therapeutic targets for spinal stenosis and to analyze their possible mechanisms and potential side effects.We conducted the Mendelian randomization analysis to identify potential drug targets for the management of spinal stenosis. Cis-expressed quantitative trait loci (cis-eQTL) data as genetic instrumental variables were acquired from the eQTLGen consortium. The summary statistics for single nucleotide polymorphism (SNP) associations of spinal stenosis were obtained from the FinnGen study(20,807 cases and 294,770 controls). Co-localization analysis was performed to determine whether there was shared causal variation between the SNPs associated with spinal stenosis as well as the eQTL. Multiple external validations were performed to reinforce the reliability and stability of the findings utilizing the cis-eQTL from the GTEx portal, the Ferkingstad et al. pQTL dataset, and the Sun et al. pQTL dataset. The viability of the identified drug targets for future clinical applications was elucidated through the phenome-wide association study and drug candidate prediction. Three drug targets (BMP6, DLK1, and GFPT1) exhibited significant causal associations with spinal stenosis in the eQTLGen cohort by MR analysis, which was strongly supported by the results of the co-localization analysis. The causal association of DLK1 and GFPT1 with spinal stenosis remained remarkable with multiple external validations. Multivariate MR and phenome-wide association study analysis indicated that both targets were not associated with other traits. In addition, phenome-wide association study analysis and drug prediction analysis demonstrated the potential of these two targets for future clinical applications. In this study, DLK1 and GFPT1 were identified as promising novel therapeutic targets for spinal stenosis, providing initial genetic insights for drug development in spinal stenosis.

PMID:39582071 | DOI:10.1038/s41598-024-80697-4