Search

Purpose The purpose of this study is to quantify the vertical shrinkage rates and the mechanical strength of three-dimensional (3D) printed parts for a variety of wood-based materials for liquid deposition modeling. Design/methodology/approach The overall hypothesis was that a well-chosen combination of binders, fibers and fillers could reduce shrinkage in the Z dimension and increase compressive and flexural strength (DIN 52185, 52186). To test this assumption, eight sub-hypotheses were formulated. Mixtures of the ingredients were chosen in different ratios to measure the performance of prints. For time efficiency, an iterative heuristic approach was used – not testing all variations of all variables in even increments, but cutting off lines of testing when mixtures were clearly performing poorly. Findings The results showed that some mixtures had high dimensional accuracy and strength, while others had neither, and others had one but not the other. Shrinkage of 3D printed objects was mainly caused by water release during drying. An increase of the wood as well as the cement, sand, salt and gypsum content led to reduced vertical shrinkage, which varied between 0 and 23%. Compressive and flexural strength showed mixed trends. An increase in wood and salt content worsened both strength properties. The addition of fibers improved flexural, and the addition of cement improved compression strength. The highest strength values of 14 MPa for compressive and 8 MPa for flexural strength were obtained in the test series with gypsum. Originality/value This paper is an important milestone in the development of environmentally friendly materials for additive manufacturing. The potential of many ingredients to improve physical properties could be demonstrated.

Surgical resection is the only potential curative treatment for patients with pancreatic ductal adenocarcinoma (PDAC), but unfortunately most patients recur within 5 years of surgery. This article aims to assess the practice patterns across major academic institutions and develop consensus recommendations for postoperative imaging and interpretation in patients with PDAC.The consensus recommendations for postoperative imaging surveillance following PDAC resection were developed using the Delphi method. Members of the Society of Abdominal Radiology (SAR) PDAC Disease Focused Panel (DFP) underwent three rounds of surveys followed by live webinar group discussions to develop consensus recommendations.Significant variations currently exist in the postoperative surveillance of PDAC, even among academic institutions. Differentiating common postoperative inflammatory and fibrotic changes from tumor recurrence remains a diagnostic challenge, and there is no reliable size threshold or growth rate of imaging findings that can provide differentiation. A new liver lesion or peritoneal nodule should be considered suspicious for tumor recurrence, and the imaging features should be interpreted in the appropriate clinical context (e.g., CA 19-9, clinical presentation, pathologic staging).Postoperative imaging following PDAC resection is challenging to interpret due to the presence of confounding postoperative inflammatory changes. A standardized reporting template for locoregional findings and report impression may improve communication of relaying risk of recurrence with referring providers, which merits validation in future studies.

Imaging and endoscopy play several important roles in the diagnosis and management of pancreatic ductal adenocarcinoma (PDAC). Computed tomography (CT) and endoscopic ultrasound play complimentary roles in the initial diagnosis and pathologic confirmation of PDAC. Endoscopy can also be used to manage biliary obstruction and gastrointestinal complications. MRI and fluorodeoxyglucose-PET-CT are typically used as problem-solving tools for complex cases. Neoadjuvant chemotherapy and surgery are often selected based on imaging findings related to vascular involvement by tumors and invasion of adjacent structures. Posttreatment surveillance imaging is used to monitor for complications, local recurrence, and systemic metastasis.

OBJECTIVE Cancer patients with spinal metastases may undergo surgery without clear assessments of prognosis, thereby impacting the optimal palliative strategy. Because the morbidity of surgery may adversely impact recovery and initiation of adjuvant therapies, evaluation of risk factors associated with mortality risk and complications is critical. Evaluation of body composition of cancer patients as a surrogate for frailty is an emerging area of study for improving preoperative risk stratification. METHODS To examine the associations of muscle characteristics and adiposity with postoperative complications, length of stay, and mortality in patients with spinal metastases, the authors designed an observational study of 484 cancer patients who received surgical treatment for spinal metastases between 2010 and 2019. Sarcopenia, muscle radiodensity, visceral adiposity, and subcutaneous adiposity were assessed on routinely available 3-month preoperative CT images by using a validated deep learning methodology. The authors used k-means clustering analysis to identify patients with similar body composition characteristics. Regression models were used to examine the associations of sarcopenia, frailty, and clusters with the outcomes of interest. RESULTS Of 484 patients enrolled, 303 had evaluable CT data on muscle and adiposity (mean age 62.00 ± 11.91 years; 57.8% male). The authors identified 2 clusters with significantly different body composition characteristics and mortality risks after spine metastases surgery. Patients in cluster 2 (high-risk cluster) had lower muscle mass index (mean ± SD 41.16 ± 7.99 vs 50.13 ± 10.45 cm2/m2), lower subcutaneous fat area (147.62 ± 57.80 vs 289.83 ± 109.31 cm2), lower visceral fat area (82.28 ± 48.96 vs 239.26 ± 98.40 cm2), higher muscle radiodensity (35.67 ± 9.94 vs 31.13 ± 9.07 Hounsfield units [HU]), and significantly higher risk of 1-year mortality (adjusted HR 1.45, 95% CI 1.05–2.01, p = 0.02) than individuals in cluster 1 (low-risk cluster). Decreased muscle mass, muscle radiodensity, and adiposity were not associated with a higher rate of complications after surgery. Prolonged length of stay (> 7 days) was associated with low muscle radiodensity (mean 30.87 vs 35.23 HU, 95% CI 1.98–6.73, p < 0.001). CONCLUSIONS Body composition analysis shows promise for better risk stratification of patients with spinal metastases under consideration for surgery. Those with lower muscle mass and subcutaneous and visceral adiposity are at greater risk for inferior outcomes.

Community-engagement is a key step in conducting research which is impactful for patients and communities. The Delaware Clinical and Translational Research (DE-CTR), Accelerating Clinical and Translational Research (ACCEL) program has implemented several successful approaches to engage our community, and to educate and motivate our researchers in this area. Increased participation in community-engaged research and community-based participatory research was accomplished through DE-CTR/ACCEL using multiple methods detailed in this manuscript. The community engagement infrastructure has fostered community involvement in translational research including capacity development, implementation, evaluation and dissemination. Academic-community partnerships for research, such as those implemented in ACCEL will be crucial to addressing health disparities and health priorities.

Pancreatic cancer is a leading cause of cancer-related deaths worldwide with increasing incidence. Early diagnosis of pancreatic cancer is a key challenge, as the disease is typically detected at a late stage. However, patients who present with early-stage disease can be cured by a combination of surgery, chemotherapy and radiotherapy. Thus, a better understanding of the risk factors for pancreatic cancer and detection at early stages has great potential to improve patient survival and reduce overall mortality from this aggressive malignancy. Here we exploit the power of advanced machine learning (ML) technology by focusing on the time sequence of clinical events and by predicting the risk of cancer occurrence over a multi-year time interval. This investigation was initially carried out using the Danish National Patient Registry (DNPR) and data which covers 41 years (1977 to 2018) of clinical records for 8.6 million patients, of which about 40,000 had a diagnosis of pancreatic cancer. To maximize predictive information extraction from these records we tested a range of ML methods, ranging from regression methods and ML without or with time dependence to time series methods such as GRU and Transformer. We explicitly train machine learning models on the time sequence of diseases in patient clinical histories and test the ability to predict cancer occurrence in time intervals of 3 to 60 months after risk assessment. For cancer occurrence within 36 months, the performance of the best model (AUROC=0.88; OR=47.5 for 20% recall and OR=159.0 for 10% recall), substantially exceeds that of a model without time information, even when disease events within a 3 month window before cancer diagnosis are excluded from training (AUROC[3m]=0.84). Independent training and testing on the Boston dataset reaches comparable performance (AUROC=0.87, OR=112.0 for 20% recall and OR=162.4 for 10% recall). We also extract from the AI machine an estimate of the contribution to prediction of individual disease features, e.g., obesity and diabetes. These results raise the state-of-the-art level of performance of cancer risk prediction on real-world data sets and provide support for the design of future screening trials for high-risk patients. AI on real-world clinical records has the potential to shift focus from treatment of late-stage to early-stage cancer, benefiting patients by improving lifespan and quality of life. We expect further increases in prediction accuracy with the availability of data beyond disease codes, such as prescriptions, laboratory values, and images. Citation Format: Davide Placido, Bo Yuan, Jessica X. Hjaltelin, Amalie D. Haue, Piotr J. Chmura, Chen Yuan, Jihye Kim, Renato Umeton, Gregory Antell, Alexander Chowdhury, Alexandra Franz, Lauren Brais, Elizabeth Andrews, Debora S. Marks, Aviv Regev, Peter Kraft, Brian M. Wolpin, Michael Rosenthal, Søren Brunak, Chris Sander. AI predicts risk of pancreatic cancer from disease trajectories using real-world electronic health records (EHRs) from Denmark and the USA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB550.

Free clinics provide healthcare to underserved patient populations, playing a critical role in the medical safety-net. Syracuse, New York has notable racial, socioeconomic, and educational disparities and is home to four free clinics. Little is known about these clinics’ patient population. This study attempts to better define this population and the barriers they face accessing traditional care. We developed a 27-question survey investigating patient demographics, barriers to traditional healthcare, and experience at local free clinics. Our analysis included descriptive statistics, t-tests, one-way ANOVA and Chi square testing. Of 287 patients surveyed, 55% of patients were employed, 78% were uninsured, and 43% cited cost as their primary barrier to insurance. 29% rated their health as fair or poor. 21% had been to the Emergency Room (ER) in the past six months. 38% stated they would go to the ER if free clinics did not exist. Insurance coverage was unrelated to education or employment status (p = .52 and .81, respectively), but differed significantly between racial and ethnic groups (p

Neoadjuvant therapy has shown value in various cancer types. The role of neoadjuvant therapy in pancreatic ductal adenocarcinoma (PDAC), however, remains unknown. The aim of the present work is to evaluate the effect of neoadjuvant therapy on the survival of patients with borderline-resectable PDAC.Between 2004 and 2015, 7730 patients with resectable PDAC and 1980 patients with borderline-resectable PDAC were identified from the National Cancer Database (NCDB). Survival was compared between resectable and borderline-resectable patients. Survival and pathologic characteristics were also compared within borderline-resectable patients who received neoadjuvant therapy and those who received adjuvant therapy alone. Kaplan-Meier method and Cox proportional-hazard models were used for analysis.Median overall survival (mOS) of all patients with resectable PDAC was similar to that of patients with borderline-resectable disease treated with neoadjuvant therapy (26.5 versus 25.7 months, p = 0.78). Patients with borderline-resectable disease treated with neoadjuvant therapy had improved mOS compared with borderline-resectable patients treated with adjuvant therapy alone (25.7 versus 19.6 months, p 0.0001). When comparing patients with borderline-resectable disease who received neoadjuvant therapy versus those who received adjuvant therapy alone, the former less often had node-positive pancreatic cancer (40.6% versus 76.3%, p 0.001) and margin-positive resections (17.8% versus 44.4%, p 0.001).Neoadjuvant therapy is associated with enhanced survival in patients with borderline-resectable pancreatic cancer, which may be attributed to tumor downstaging.

Liquid Deposition Modeling is introduced as a promising technology for 3D printing of wood. Specimens were printed using different paste-like suspensions made from ground beech sawdust and methylcellulose dissolved in water. The wood content could be increased up to 89% in dry mass. Physical properties were influenced by binder/water ratio and wood particle size. Shrinkage due to drying was 17.3–20.0%. Density values (r12) lay between 0.33 and 0.48 g/cm3. Bending strength and modulus of elasticity ranged from 2.3 to 7.4 and from 284.8 to 733.1 N/mm2, respectively. Density, MOR and MOE increased with increasing viscosity of dissolved methylcellulose and decreased with increasing particle size.

Primary cardiac sarcomas (PCSs) are extremely rare entities with poor prognosis. We present two patients with undifferentiated pleomorphic sarcoma. (1) 79 year old female with hypertension presented with congestive heart failure. Transthoracic echocardiogram (TTE) and cardiac MRI revealed an

The problem of 3D chromosome structure inference from Hi-C datasets is important and challenging. While bulk Hi-C datasets contain contact information derived from millions of cells, and can capture major structural features shared by the majority of cells in the sample, they do not provide information about local variability between cells. Single cell Hi-C can overcome this problem, but contact matrices are generally very sparse, making structural inference more problematic. We have developed a Bayesian multiscale approach, named SIMBA3D, to infer 3D structures of chromosomes from single cell Hi-C while including the bulk Hi-C data and some regularization terms as a prior. We study the landscape of solutions for each single-cell Hi-C dataset as a function of prior strength and demonstrate clustering of solutions using data from the same cell.

Since autism spectrum disorder (ASD) is often comorbid with psychiatric disorders, children who no longer meet criteria for ASD (optimal outcome; OO) may still be at risk for psychiatric disorders. A parent interview for DSM-IV psychiatric disorders (K-SADS-PL) for 33 OO, 42 high-functioning autism (HFA) and 34 typically developing (TD) youth, ages 8-21, showed that OO and HFA groups had elevated current ADHD and specific phobias, with tics in HFA. In the past, the HFA group also had elevated depression and ODD, and the OO group had tics. The HFA group also showed subthreshold symptoms of specific and social phobias, and generalized anxiety. Psychopathology in the OO group abated over time as did their autism, and decreased more than in HFA.

Within the temperature range from 160 to 220 $$^\circ $$ C, an increase of 20 K doubled the mass loss of thermally modified African alpine bamboo (Yushania alpina). It reflected the decomposition of hemicelluloses and correlated with the deterioration of impact resistance. The treatment temperatures had a stronger effect than the time of modification.

This article studies the problem of modeling relationship between two spherical (or directional) random variables in a regression setup. Here the predictor and the response variables are constrained to be on a unit sphere and, due to this nonlinear condition, the standard Euclidean regression models do not apply. Several past papers have studied this problem, termed spherical regression, by modeling the response variable with a von Mises-Fisher (VMF) density with the mean given by a rotation of the predictor variable. The few papers that go beyond rigid rotations are limited to one- or two-dimensional spheres. This article extends the mean transformations to a larger group—the projective linear group of transformations—on unit spheres of arbitrary dimensions, while keeping the VMF density to model the noise. It develops a Newton–Raphson algorithm on the special linear group for estimating the MLE of regression parameter and establishes its asymptotic properties when the sample-size becomes large. Through ...

Some individuals who lose their autism spectrum disorder (ASD) diagnosis may continue to display subtle weaknesses in language. We examined language and verbal memory in 44 individuals with high-functioning autism (HFA), 34 individuals with “optimal outcomes” (OO) and 34 individuals with typical development (TD). The OO group scored in the average range or above on all measures and showed few differences from the TD group. The HFA group performed within the average range but showed significantly lower mean performance than the other groups on multiple language measures, even when controlling for VIQ. Results also indicate that OO individuals show strong language abilities in all areas tested, but that their language may show greater reliance on verbal memory.

Executive functioning (EF) is examined among children and adolescents once diagnosed with an autism spectrum disorder (ASD), but who no longer meet diagnostic criteria. These individuals have average social and language skills, receive minimal school support and are considered to have achieved "optimal outcomes" (OOs). Since residual impairments in these individuals might be expected in deficits central to autism, and in developmentally advanced skills, EF was examined in 34 individuals who achieved OOs, 43 individuals with high-functioning autism (HFA), and 34 typically developing (TD) peers. Groups were matched on age (M = 13.49), gender, and nonverbal IQ (NVIQ) but differed on verbal IQ (VIQ; HFA < TD, OO). On direct assessment, all three groups demonstrated average EF; however, the OO and HFA groups exhibited more impulsivity and less efficient planning and problem-solving than the TD group, and more HFA participants exhibited below average inhibition than did OO and TD participants. Parent-report measures revealed average EF among the OO and TD groups; however, the OO group exhibited more difficulty than the TD group on set-shifting and working memory. HFA participants demonstrated more difficulty on all parent-reported EF domains, with a clinical impairment in attention-shifting. Results suggest that EF in OO appears to be within the average range, even for functions that were impaired among individuals with HFA. Despite their average performance, however, the OO and TD groups differed on measures of impulsivity, set-shifting, problem-solving, working memory, and planning, suggesting that the OO group does not have the above-average EF scores of the TD group despite their high-average IQs.

Background: Although autism spectrum disorders (ASDs) are generally considered lifelong disabilities, literature suggests that a minority of individuals with an ASD will lose the diagnosis. However, the existence of this phenomenon, as well as its frequency and interpretation, is still controversial: were they misdiagnosed initially, is this a rare event, did they lose the full diagnosis, but still suffer significant social and communication impairments or did they lose all symptoms of ASD and function socially within the normal range? Methods: The present study documents a group of these optimal outcome individuals (OO group, n = 34) by comparing their functioning on standardized measures to age, sex, and nonverbal IQ matched individuals with high-functioning autism (HFA group, n = 44) or typical development (TD group, n = 34). For this study, ‘optimal outcome’ requires losing all symptoms of ASD in addition to the diagnosis, and functioning within the nonautistic range of social interaction and communication. Domains explored include language, face recognition, socialization, communication, and autism symptoms. Results: Optimal outcome and TD groups’ mean scores did not differ on socialization, communication, face recognition, or most language subscales, although three OO individuals showed below-average scores on face recognition. Early in their development, the OO group displayed milder symptoms than the HFA group in the social domain, but had equally severe difficulties with communication and repetitive behaviors. Conclusions: Although possible deficits in more subtle aspects of social interaction or cognition are not ruled out, the results substantiate the possibility of OO from autism spectrum disorders and demonstrate an overall level of functioning within normal limits for this group. Keywords: Autism, outcome, optimal.

For purposes of wood preservation, stabilization and fire protection, the penetration ability of bio-inspired environmentally friendly Sebosil® nanosols in axial direction of the pine tracheids were investigated using macroscopic evaluations and EDX microanalysis.

This paper investigates the architecture designed by transported convict, Francis Greenway, and erected under the reforming Governorship of General Lachlan Macquarie in Sydney from 1815 onwards. It analyses how that architecture articulated Macquarie's ideas on how the planning and fabric of the city could facilitate the policies of reformation that he was implementing, and shows how this could have been perceived as oppositional to British Government policy after 1815. It affects this principally through considering the report of Commissioner of Inquiry, John Thomas Bigge, who arrived in Sydney in 1819, other literary sources, and the urban fabric of Sydney itself. In addition, it considers contemporary thinking on the built environment and social control, particularly as manifested in Scotland. It closes with more general observations on the ways in which Sydney contrasted with London, which city was perceived as falling short of what was expected of a great metropolis, and how its early nineteenth-century evolution into a new type of city itself affected the way in which Sydney was perceived.

Autism spectrum disorders (ASD) often go undetected in toddlers. The Modified Checklist for Autism in Toddlers (M-CHAT) was used to screen 3,793 children aged 16–30 months from low- and high-risk sources; screen positive cases were diagnostically evaluated. Re-screening was performed on 1,416 children aged 42–54 months. Time 1 Positive Predictive Value (PPV) was .36 for the initial screening and .74 for the screening plus follow-up telephone interview; values were similar for Time 2 PPV. When separating referral sources, PPV was low for the low-risk sample but acceptable with the follow-up telephone interview. Children with ASD from the low-risk and high-risk samples were highly similar. Results indicate that the M-CHAT continues to be a promising instrument for the early detection of ASD.