bluuebunny/crossref_metadata_2025_split huggingface dataset:
Macrotrabecular-massive subtype-based nomogram to predict early recurrence of hepatocellular carcinoma after surgery
Authors: Yang Yong , Mao Shuqi , Fang Jiongze , Wang Gaoqing , Shan Yuying , Yu Xi , Lu Caide 
Published: February 2023

Objectives
To analyze the predictive factors on early postoperative recurrence of hepatocellular carcinoma (HCC) and to establish a new nomogram to predict early postoperative recurrence of HCC.

Methods
A retrospective analysis of 383 patients who had undergone curative resection between February 2012 and September 2020 in our center was performed. The Kaplan–Meier method was used for survival curve analysis. Univariate and multivariate Cox regression were performed to identify independent risk factors associated with early recurrence, and a nomogram for predicting early recurrence of HCC was established.

Results
A total of 152/383 patients developed recurrence after surgery, of which 83 had recurrence within 1 year. Multivariate Cox regression analysis showed that preoperative alpha-fetoprotein level ≥400 ng/ml (P = 0.001), tumor diameter ≥5 cm (P = 0.009) and MVI (P = 0.007 and macrotrabecular-massive HCC (P = 0.003) were independent risk factors for early postoperative recurrence of HCC. The macrotrabecular-massive-based nomogram obtained a good C-index (0.74) for predicting early recurrence of HCC, and the area under the curve for predicting early recurrence was 0.767, which was better than the single American Joint Committee on Cancer T stage and Barcelona Clinic Liver Cancer stage.

Conclusions
The nomogram based on macrotrabecular-massive HCC can effectively predict early postoperative recurrence of HCC.
Role of textural analysis parameters derived from FDG PET/CT in differentiating hepatocellular carcinoma and hepatic metastases
Authors: Mishra Ajit , Ravina Mudalsha , Kote Rutuja , Kumar Amit , Kashyap Yashwant , Dasgupta Subhajit , Reddy Moulish 
Published: February 2023

Introduction
Texture and radiomic analysis characterize the tumor’s phenotype and evaluate its microenvironment in quantitative terms. The aim of this study was to investigate the role of textural features of 18F-FDG PET/computed tomography (CT) images in differentiating hepatocellular carcinoma (HCC) and hepatic metastasis in patients with suspected liver tumors.

Methods
This is a retrospective, single-center study of 30 patients who underwent FDG PET/CT for the characterization of liver lesions or for staging a suspected liver tumor. The histological diagnosis of either primary or metastatic tumor was obtained from CT-guided biopsy, ultrasound-guided biopsy, or surgical removal of a liver lesion. The PET/CT images were then processed in commercially available textural analysis software. Region of interest was drawn over the primary tumor with a 40% threshold and was processed further to derive 42 textural and radiomic parameters. These parameters were then compared between HCC group and hepatic metastases group. Receiver-operating characteristic (ROC) curves were used to identify cutoff values for textural features with a P value <0.05 for statistical significance.

Results
A retrospective study of 30 patients with suspected liver tumors was done. After undergoing PET/CT, the histological diagnosis of these lesions was confirmed. Among these 30 patients, 15 patients had HCC, and 15 patients had hepatic metastases from various primary sites. Seven textural analysis parameters were significant in differentiating HCC from liver metastasis. Cutoff values were calculated for these parameters according to the ROC curves, standardized uptake value (SUV) Skewness (0.705), SUV Kurtosis (3.65), SUV Excess Kurtosis (0.653), gray-level zone length matrix_long zone emphasis (349.2), gray-level zone length matrix_long zone low gray-level emphasis (1.6), gray-level run length matrix_long run emphasis (1.38) and gray-level co-occurrence matrix_Homogeneity (0.406).

Conclusion
Textural analysis parameters could successfully differentiate HCC and hepatic metastasis non-invasively. Larger multi-center studies are needed for better clinical prognostication of these parameters.
A Clinical Nurse Leader-Guided Behavior Management Consult Team to Impact Workplace Violence Against Nurses
Authors: Smith Michelle A. 
Published: February 2023

Background:
Violence against nurses is a growing problem. Minimizing violence against nurses will contribute to a safer clinical environment.

Local Problem:
An increase in violence against nurses was identified at a regional medical center in the Midwest.

Methods:
A clinical nurse leader (CNL)–led behavior management consult team was developed to support the clinical team with aggressive, disruptive, or violent patients. The team consulted, assigned interventions to address potential source(s) of agitation, provided clinical support and guidance, and developed clinical tools to manage future patients with similar behavior patterns.

Results:
Between April 2019 and January 2023, a total of 108 behavior management consults were completed. Staff appreciated the additional support from the team.

Conclusions:
Implementation of a CNL-led behavior management consult team was found to be beneficial; other health care systems should consider implementing a similar consult team.
“Histological and Immunohistochemical Evaluation of Human Breast Capsules Formed Around Five Different Expander Surfaces”
Authors: Cagli Barbara , Carotti Simone , Segreto Francesco , Francesconi Maria , Marangi Giovanni F. , Tenna Stefania , Diomedi Michele , Perrone Giuseppe , Morini Sergio , Persichetti Paolo 
Published: February 2023

Background:
Polyurethane (PU) coating and implant texturization were designed to reduce the incidence of capsular contracture (CC), even if the link between surface type and CC remains unclear. To date the etiopathogenetic aspects have not been fully clarified. The aim of this study was to evaluate capsules formed around 5 different breast expanders.

Methods:
Thirty patients were divided into randomized groups implanted with 5 different expanders: Smooth, coated with PU foam (Poly), with a low-microtextured, high-microtextured, and macrotextured surface (L-Micro, H-Micro, Macro). Specimens of the capsules were removed at implant reconstruction and evaluated for morphology and immunohistochemistry expression of α-smooth muscle actin (α-SMA), collagen type I and III, CD68, CD34, and CD3. Remodeling combined index was also evaluated.

Results:
Expression of α-SMA was significantly increased in Smooth capsules versus Poly, L-Micro, and H-Micro groups (p=0.007; p=0.010; p=0.028), while the prevalence of collagen type I in Smooth capsules and collagen type III in Poly capsules identified a stable versus an unstable tissue. Remodeling combined index and α-SMA showed an inverted correlation. CD68 and CD34 cellular expression increased significantly in Poly capsules with respect to Smooth (p<0.001; p<0.001) and Macro groups (p<0.001; p<0.001). CD3 showed no significant difference among the groups.

Conclusions:
In this human study we observed that an increased tissue remodeling and a reduced myofibroblasts activation, along with the inflammatory infiltration and the neo-angiogenesis, especially in Poly and L-Micro groups, might promote the formation of an unstable and less fibrotic capsule, lowering the risk of CC.
Impact of Vasodilation on Oxygen-Enhanced Functional Lung MRI at 0.55 T
Authors: Wieslander Björn , Seemann Felicia , Javed Ahsan , Bruce Christopher G. , Ramasawmy Rajiv , Jaimes Andrea , Lucas Katherine , Frasier Victoria , O'Brien Kendall J. , Potersnak Amanda , Khan Jaffar M. , Schenke William H. , Chen Marcus Y. , Lederman Robert J. , Campbell-Washburn Adrienne E. 
Published: February 2023

Background
Oxygen-enhanced magnetic resonance imaging (OE-MRI) can be used to assess regional lung function without ionizing radiation. Inhaled oxygen acts as a T1-shortening contrast agent to increase signal in T1-weighted (T1w) images. However, increase in proton density from pulmonary hyperoxic vasodilation may also contribute to the measured signal enhancement. Our aim was to quantify the relative contributions of the T1-shortening and vasodilatory effects of oxygen to signal enhancement in OE-MRI in both swine and healthy volunteers.

Methods
We imaged 14 anesthetized female swine (47 ± 8 kg) using a prototype 0.55 T high-performance MRI system while experimentally manipulating oxygenation and blood volume independently through oxygen titration, partial occlusion of the vena cava for volume reduction, and infusion of colloid fluid (6% hydroxyethyl starch) for volume increase. Ten healthy volunteers were imaged before, during, and after hyperoxia. Two proton density–weighted (PDw) and 2 T1w ultrashort echo time images were acquired per experimental state. The median PDw and T1w percent signal enhancement (PSE), compared with baseline room air, was calculated after image registration and correction for lung volume changes. Differences in median PSE were compared using Wilcoxon signed rank test.

Results
The PSE in PDw images after 100% oxygen was similar in swine (1.66% ± 1.41%, P = 0.01) and in healthy volunteers (1.99% ± 1.79%, P = 0.02), indicating that oxygen-induced pulmonary vasodilation causes ~2% lung proton density increase. The PSE in T1w images after 100% oxygen was also similar (swine, 9.20% ± 1.68%, P < 0.001; healthy volunteers, 10.10% ± 3.05%, P < 0.001). The PSE in T1w enhancement was oxygen dose-dependent in anesthetized swine, and we measured a dose-dependent PDw image signal increase from infused fluids.

Conclusions
The contribution of oxygen-induced vasodilation to T1w OE-MRI signal was measurable using PDw imaging and was found to be ~2% in both anesthetized swine and in healthy volunteers. This finding may have implications for patients with regional or global hypoxia or vascular dysfunction undergoing OE-MRI and suggest that PDw imaging may be useful to account for oxygen-induced vasodilation in OE-MRI.
Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMI
Authors: Zhou Juexiao , Zhou Longxi , Wang Di , Xu Xiaopeng , Li Haoyang , Chu Yuetan , Han Wenkai , Gao Xin 
Published: February 2023

AbstractHeterogeneous data is endemic due to the use of diverse models and settings of devices by hospitals in the field of medical imaging. However, there are few open-source frameworks for federated heterogeneous medical image analysis with personalization and privacy protection simultaneously without the demand to modify the existing model structures or to share any private data. In this paper, we proposed PPPML-HMI, an open-source learning paradigm for personalized and privacy-preserving federated heterogeneous medical image analysis. To our best knowledge, personalization and privacy protection were achieved simultaneously for the first time under the federated scenario by integrating the PerFedAvg algorithm and designing our novel cyclic secure aggregation with the homomorphic encryption algorithm. To show the utility of PPPML-HMI, we applied it to a simulated classification task namely the classification of healthy people and patients from the RAD-ChestCT Dataset, and one real-world segmentation task namely the segmentation of lung infections from COVID-19 CT scans. For the real-world task, PPPML-HMI achieved ∼5% higher Dice score on average compared to conventional FL under the heterogeneous scenario. Meanwhile, we applied the improved deep leakage from gradients to simulate adversarial attacks and showed the solid privacy-preserving capability of PPPML-HMI. By applying PPPML-HMI to both tasks with different neural networks, a varied number of users, and sample sizes, we further demonstrated the strong robustness of PPPML-HMI.
Association of Cancer History with Structural Brain Aging Markers of Alzheimer’s Disease and Dementia Risk
Authors: Wang Jingxuan , Sims Kendra D. , Ackley Sarah F. , Chen Ruijia , Kobayashi Lindsay C. , Hayes-Larson Eleanor , Mayeda Elizabeth Rose , Buto Peter , Zimmerman Scott C. , Graff Rebecca E. , Glymour M. Maria 
Published: February 2023

AbstractBackground and ObjectivesCancer survivors are less likely than comparably-aged individuals without a cancer history to develop Alzheimer’s disease and related dementias (ADRD). We investigated the association between cancer history and structural magnetic resonance imaging (MRI) markers for ADRD risk, using linear mixed-effects models to assess differences at the mean values of MRI markers and quantile regression to examine whether the association varies across the distribution of MRI markers of brain aging.MethodsAmong UK Biobank participants with ≥1 brain MRI, we considered total gray matter volume, total brain volume, hippocampal volume, white matter hyperintensity volume, and mean cortical thickness in the Alzheimer’s disease (AD) signature region. Cancer history was ascertained from national registry and self-report. We first specified linear mixed models with random intercepts to assess mean differences in MRI markers according to cancer history. Next, to examine whether effects of cancer history on these markers varies across the ADRD risk distribution, we specified quantile regression models to assess differences in quantile cut-points of the distribution of MRI markers according to cancer history. Models adjusted for demographics, APOE-ε4 status, and health behaviors.ResultsThe sample included 42,242 MRIs on 37,588 participants with no cancer history (mean age 64.1 years), and 6,073 MRIs on 5,514 participants with a cancer diagnosis prior to MRI (mean age 66.7 years). Cancer history was associated with smaller mean hippocampal volume (b=-19 mm3, 95% confidence interval [CI]=-36, -1) and lower mean cortical thickness in the AD signature region (b=-0.004 mm, 95% CI=-0.007, -0.000). Quantile regressions indicated cancer history had larger effects on high quantiles of white matter hyperintensities (10thpercentile b=-49 mm3, 95% CI=-112, 19; 90thpercentile b=552 mm3, 95% CI= 250, 1002) and low quantiles of cortical thickness (10thpercentile b=-0.006 mm, 95% CI=-0.011, -0.000; 90thpercentile b=0.003 mm3, 95% CI=-0.003, 0.007), indicating individuals most vulnerable to ADRD were more affected by cancer history.DiscussionWe found no evidence that cancer history was associated with less ADRD-related neurodegeneration. To the contrary, adults with cancer history had worse MRI indicators of dementia risk. Adverse associations were largest in the highest-risk quantiles of neuroimaging markers.
Structural and functional pathology in cocaine use disorder with polysubstance use: a multimodal fusion approach
Authors: Rasgado-Toledo Jalil , Duvvada Sai Siddharth , Shah Apurva , Ingalhalikar Madhura , Alluri Vinoo , Garza-Villarreal Eduardo A. 
Published: February 2023

AbstractCocaine use disorder (CUD) is described as a compulsive urge to seek and consume cocaine despite the inimical consequences. MRI studies from different modalities have shown that CUD patients exhibit structural and/or functional connectivity pathology among several brain regions. Nevertheless, both connectivities are commonly studied and analyzed separately, which may potentially obscure its relationship between them, and with the clinical pathology. Here, we compare and contrast structural and functional brain networks in CUD patients and healthy controls (HC) using multimodal fusion. The sample consisted of 63 (8 females) CUD patients and 42 (9 females) healthy controls (HC), recruited as part of the SUDMEX CONN database. For this, we computed a battery of graph-based measures from multi-shell diffusion-weighted imaging and resting state fc-fMRI to quantify local and global connectivity. Then we used multimodal canonical component analysis plus joint independent component analysis (mCCA+jICA) to compare between techniques, and evaluate group differences and its association with clinical alteration. Unimodal results showed a striatal decrease in the participation coefficient, but applied supervised data fusion revealed other regions with cocaine-related alterations in joint functional communication. When performing multimodal fusion analysis, we observed a higher centrality of the interrelationship and a lower participation coefficient in patients with CUD. In contrast to the unimodal approach, the multimodal fusion method was able to reveal latent information about brain regions involved in impairment due to cocaine abuse. The present results could help in understanding the pathology of CUD in order to develop better pre-treatment/post-treatment intervention designs.
Deciphering causal proteins in Alzheimer’s disease: A novel Mendelian randomization method integrated with AlphaFold3 for 3D structure prediction
Authors: Yao Minhao , Miller Gary W. , Vardarajan Badri N. , Baccarelli Andrea A. , Guo Zijian , Liu Zhonghua 
Published: February 2023

SummaryHidden confounding biases hinder identifying causal protein biomarkers for Alzheimer’s disease in non-randomized studies. While Mendelian randomization (MR) can mitigate these biases using protein quantitative trait loci (pQTLs) as instrumental variables, some pQTLs violate core assumptions, leading to biased conclusions. To address this, we propose MR-SPI, a novel MR method that selects valid pQTL instruments using the Anna Karenina Principle and performs robust post-selection inference. Integrating MR-SPI with AlphaFold3, we developed a computational pipeline to identify causal protein biomarkers and predict 3D structural changes. Applied to genome-wide proteomics data from 54,306 UK Biobank participants and 455,258 subjects (71,880 cases and 383,378 controls) for a genome-wide association study of Alzheimer’s disease, we identified seven proteins (TREM2, PILRB, PILRA, EPHA1, CD33, RET, and CD55) with structural alterations due to missense mutations. These findings offer insights into the etiology and potential drug targets for Alzheimer’s disease.
The association between multidimensional sleep health and gestational weight gain: nuMoM2b Sleep Duration and Continuity Study
Authors: Hawkins Marquis S. , Pokutnaya Darya Y. , Bodnar Lisa M. , Levine Michele D. , Buysse Daniel J. , Davis Esa M. , Wallace Meredith L. , Zee Phyllis C. , Grobman William A. , Reid Kathryn J. , Facco Francesca L. 
Published: February 2023

AbstractBackgroundAlthough poor sleep health is associated with weight gain and obesity in the non-pregnant population, research on the impact of sleep health on weight change among pregnant people using a multidimensional sleep-health framework is needed. This study examined associations among mid-pregnancy sleep health indicators, multidimensional sleep health, and gestational weight gain (GWG).MethodsWe conducted a secondary data analysis of the Nulliparous Pregnancy Outcome Study: Monitoring Mothers-to-be Sleep Duration and Continuity Study (n=745). Indicators of individual sleep domains (i.e., regularity, nap duration, timing, efficiency, and duration) were assessed via actigraphy between 16 and 21 weeks of gestation. We defined “healthy” sleep in each domain based on empirical thresholds. Multidimensional sleep health was based on sleep profiles derived from latent class analysis. Total GWG, the difference between self-reported pre-pregnancy weight and the last measured weight before delivery, was converted to z-scores using gestational age- and BMI-specific charts. GWG was defined as low (<−1 SD), moderate (−1 or +1 SD), and high (>+1 SD).ResultsNearly 50% of the participants had a healthy sleep profile (i.e., healthy sleep in most domains), whereas others had a sleep profile defined as having varying degrees of poor health in each domain. While indicators of individual sleep domains were not associated with GWG, multidimensional sleep health was related to low and high GWG. Participants with a sleep profile characterized as having low efficiency, late timing, and long sleep duration (vs. healthy sleep profile) had a higher risk (RR 1.7; 95% CI 1.0, 3.1) of low GWG a lower risk of high GWG (RR 0.5 95% CI 0.2, 1.1) (vs. moderate GWG).ConclusionsMultidimensional sleep health was more strongly associated with GWG than individual sleep domains. Future research should determine whether sleep health is a valuable intervention target for optimizing GWG.SynopsisStudy question-What is the association between mid-pregnancy multidimensional sleep health and gestational weight gain?What’s already known?-Sleep is associated with weight and weight gain outside of pregnancyWhat does this study add?-We identified patterns of sleep behaviors associated with an increased risk of low gestational weight gain
The Q-IMPROvE (Queensland-IMplementation of PRecision Oncology in brEast cancer) pilot study
Authors: McCart Reed Amy E , McCurry Therese , Hollway Georgina , Al-Saig Haidar , Andelkovic Vladimir , Cuff Katharine , Cummings Margaret , Fairbairn David , Inglis Po-ling , Jagger Gillian , Johanson Helene , Kalinowski Lauren , Kondrashova Olga , Koufariotis Lambros T. , Kuchel Anna , Ladwa Rahul , Lau Chiyan , Lundie Ben , Fan Helen Mar , McCarthy Nicole , Middleton Kathryn , Murugappan Kowsalya , Nalder Mark , Niland Colleen , Nottage Michelle K , O’Byrne Kenneth J , Pearson John V , Roberts Kate , Hormaechea Gorane Santamaria , Snell Cameron , Steinke Karin , Suder Aneta , Tam Diana , Walpole Euan , Woodward Natasha , Wong Clement , Wong Ho Yi , Xu Wen , Simpson Peter T , Waddell Nicola , Lakhani Sunil R 
Published: February 2023

AbstractBackgroundThe cancer genomics field has embraced the advent of precision oncology, and vast volumes of data have been mined for biomarkers of drug actionability. While some cancers, such as lung cancer, have detailed panels of actionable genomic biomarkers, sequencing panels have been less useful in breast cancer given its large number of cancer driver genes mutated at a relatively low frequency. Furthermore, mutation signatures have potential to assist in identifying homologous recombination deficient tumours for targeting with PARP inhibitor therapy.Patients and MethodsTo investigate whether whole genome sequencing could benefit breast cancer patients we initiated the Q-IMPROvE (Queensland-IMplementation of PRecision Oncology in brEast cancer) prospective pilot study. We report the analysis of matched tumour and normal genomes of 28 high-risk breast cancer patients undergoing treatment in the neo-adjuvant setting.ResultsUsing whole genome sequencing, we detected actionable events that would otherwise not have been identified. A quarter of patients demonstrated a defect in homologous recombination DNA repair using the HRDetect and HRD scores. Germline variants of importance (BRCA1, CHEK2) were identified in two patients that did not meet clinical guidelines for germline genetic testing. Somatically,TP53andPIK3CAwere the most commonly mutated breast cancer driver genes.ConclusionsWe have demonstrated the benefit of whole genome sequencing of both the tumour and germline for breast cancer patients otherwise not meeting clinical criteria for genetic health referrals.