Empowerment or Engagement? Digital Health Technologies for Mental Healthcare

We argue that while digital health technologies (e.g. artificial intelligence, smartphones, and virtual reality) present significant opportunities for improving the delivery of healthcare, key concepts that are used to evaluate and understand their impact can obscure significant ethical issues related to patient engagement and experience. Specifically, we focus on the concept of empowerment and ask whether it is adequate for addressing some significant ethical concerns that relate to digital health technologies for mental healthcare. We frame these concerns using five key ethical principles for AI ethics (i.e. autonomy, beneficence, non-maleficence, justice, and explicability), which have their roots in the bioethical literature, in order to critically evaluate the role that digital health technologies will have in the future of digital healthcare

Current Role Of Hepatopancreatoduodenectomy For The Management Of Gallbladder Cancer And Extrahepatic Cholangiocarcinoma: A Systematic Review

BACKGROUND Hepatopancreatoduodenectomy (HPD) is the simultaneous combination of hepatic resection, pancreaticoduodenectomy, and resection of the entire extrahepatic biliary system. HPD is not a universally accepted due to high mortality and morbidity rates, as well as to controversial survival benefits. AIM To evaluate the current role of HPD for curative treatment of gallbladder cancer (GC) or extrahepatic cholangiocarcinoma (ECC) invading both the hepatic hilum and the intrapancreatic common bile duct. METHODS A systematic literature search using the PubMed, Web of Science, and Scopus databases was performed to identify studies reporting on HPD, using the following keywords: ‘Hepatopancreaticoduodenectomy’, ‘hepatopancreatoduodenectomy’, ‘hepatopancreatectomy’, ‘pancreaticoduodenectomy’, ‘hepatectomy’, ‘hepatic resection’, ‘liver resection’, ‘Whipple procedure’, ‘bile duct cancer’, ‘gallbladder cancer’, and ‘cholangiocarcinoma’. RESULTS This updated systematic review, focusing on 13 papers published between 2015 and 2020, found that rates of morbidity for HPD have remained high, ranging between 37.0% and 97.4%, while liver failure and pancreatic fistula are the most serious complications. However, perioperative mortality for HPD has decreased compared to initial experiences, and varies between 0% and 26%, although in selected center it is well below 10%. Long term survival outcomes can be achieved in selected patients with R0 resection, although 5–year survival is better for ECC than GC. CONCLUSION The present review supports the role of HPD in patients with GC and ECC with horizontal spread involving the hepatic hilum and the intrapancreatic bile duct, provided that it is performed in centers with high experience in hepatobiliarypancreatic surgery. Extensive use of preoperative portal vein embolization, and preoperative biliary drainage in patients with obstructive jaundice, represent strategies for decreasing the occurrence and severity of postoperative complications. It is advisable to develop internationally-accepted protocols for patient selection, preoperative assessment, operative technique, and perioperative care, in order to better define which patients would benefit from HPD

Contrast-enhanced ultrasound Liver Imaging Reporting and Data System: Lights and shadows in hepatocellular carcinoma and cholangiocellular carcinoma diagnosis

BACKGROUND Contrast-enhanced ultrasound (CEUS) is considered a secondary examination compared to computed tomography (CT) and magnetic resonance imaging (MRI) in the diagnosis of hepatocellular carcinoma (HCC), due to the risk of misdiagnosing intrahepatic cholangiocarcinoma (ICC). The introduction of CEUS Liver Imaging Reporting and Data System (CEUS LI-RADS) might overcome this limitation. Even though data from the literature seems promising, its reliability in real-life context has not been well-established yet. AIM To test the accuracy of CEUS LI-RADS for correctly diagnosing HCC and ICC in cirrhosis. METHODS CEUS LI-RADS class was retrospectively assigned to 511 nodules identified in 269 patients suffering from liver cirrhosis. The diagnostic standard for all nodules was either biopsy (102 nodules) or CT/MRI (409 nodules). Common diagnostic accuracy indexes such as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were assessed for the following associations: CEUS LR-5 and HCC; CEUS LR-4 and 5 merged class and HCC; CEUS LR-M and ICC; and CEUS LR-3 and malignancy. The frequency of malignant lesions in CEUS LR-3 subgroups with different CEUS patterns was also determined. Inter-rater agreement for CEUS LI-RADS class assignment and for major CEUS pattern identification was evaluated. RESULTS CEUS LR-5 predicted HCC with a 67.6% sensitivity, 97.7% specificity, and 99.3% PPV (P < 0.001). The merging of LR-4 and 5 offered an improved 93.9% sensitivity in HCC diagnosis with a 94.3% specificity and 98.8% PPV (P < 0.001). CEUS LR-M predicted ICC with a 91.3% sensitivity, 96.7% specificity, and 99.6% NPV (P < 0.001). CEUS LR-3 predominantly included benign lesions (only 28.8% of malignancies). In this class, the hypo-hypo pattern showed a much higher rate of malignant lesions (73.3%) than the iso-iso pattern (2.6%). Inter-rater agreement between internal raters for CEUS-LR class assignment was almost perfect (n = 511, k = 0.94, P < 0.001), while the agreement among raters from separate centres was substantial (n = 50, k = 0.67, P < 0.001). Agreement was stronger for arterial phase hyperenhancement (internal k = 0.86, P < 2.7 × 10-214; external k = 0.8, P < 0.001) than washout (internal k = 0.79, P < 1.6 × 10-202; external k = 0.71, P < 0.001). CONCLUSION CEUS LI-RADS is effective but can be improved by merging LR-4 and 5 to diagnose HCC and by splitting LR-3 into two subgroups to differentiate iso-iso nodules from other patterns

Structural order and magnetic anisotropy transition in Co/Fe multilayers

Co/Fe multilayers were electron-beam evaporated in ultrahigh vacuum onto quartz substrates keeping the Co layer thickness (10 nm) constant and changing that of Fe (10-30 nm). For Fe layer thicknesses up to 24 nm, the magnetization substantially lies in the film plane and shows a uniaxial magnetic anisotropy. The coercive field measured along the easy axis is similar to10 Oe, and the x-ray reflectivity patterns show a superlattice behavior. For a Fe layer thickness equal to 30 nm, the in-plane texture strongly decreases, the coercive field increases up to similar to100 Oe, the magnetization direction forms an out-of-plane angle of similar to36degrees and a stripe magnetic domain structure takes place. The observed in-plane anisotropy and the changing in the magnetic order as a function of the iron layer thickness is discussed and justified, assuming that the growth of the first Co layer occurs by the nucleation of ordered zones, influencing the subsequent layer order via exchange interaction

Scalable HPC & AI infrastructure for COVID-19 therapeutics

COVID-19 has claimed more than 2.7 × 106 lives and resulted in over 124 × 106 infections. There is an urgent need to identify drugs that can inhibit SARS-CoV-2. We discuss innovations in computational infrastructure and methods that are accelerating and advancing drug design. Specifically, we describe several methods that integrate artificial intelligence and simulation-based approaches, and the design of computational infrastructure to support these methods at scale. We discuss their implementation, characterize their performance, and highlight science advances that these capabilities have enabled

Pandemic Drugs at Pandemic Speed: Infrastructure for Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computers

The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow. There is a major bottleneck screening the vast number of potential small molecules to shortlist lead compounds for antiviral drug development. New opportunities to accelerate drug discovery lie at the interface between machine learning methods, in this case, developed for linear accelerators, and physics-based methods. The two in silico methods, each have their own advantages and limitations which, interestingly, complement each other. Here, we present an innovative infrastructural development that combines both approaches to accelerate drug discovery. The scale of the potential resulting workflow is such that it is dependent on supercomputing to achieve extremely high throughput. We have demonstrated the viability of this workflow for the study of inhibitors for four COVID-19 target proteins and our ability to perform the required large-scale calculations to identify lead antiviral compounds through repurposing on a variety of supercomputers

IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads

The drug discovery process currently employed in the pharmaceutical industry typically requires about 10 years and $2–3 billion to deliver one new drug. This is both too expensive and too slow, especially in emergencies like the COVID-19 pandemic. In silico methodologies need to be improved both to select better lead compounds, so as to improve the efficiency of later stages in the drug discovery protocol, and to identify those lead compounds more quickly. No known methodological approach can deliver this combination of higher quality and speed. Here, we describe an Integrated Modeling PipEline for COVID Cure by Assessing Better LEads (IMPECCABLE) that employs multiple methodological innovations to overcome this fundamental limitation. We also describe the computational framework that we have developed to support these innovations at scale, and characterize the performance of this framework in terms of throughput, peak performance, and scientific results. We show that individual workflow components deliver 100 × to 1000 × improvement over traditional methods, and that the integration of methods, supported by scalable infrastructure, speeds up drug discovery by orders of magnitudes. IMPECCABLE has screened ∼ 1011 ligands and has been used to discover a promising drug candidate. These capabilities have been used by the US DOE National Virtual Biotechnology Laboratory and the EU Centre of Excellence in Computational Biomedicine

The Framework for Assessing Changes To Sea-level (FACTS) v1.0: a platform for characterizing parametric and structural uncertainty in future global, relative, and extreme sea-level change

Future sea-level rise projections are characterized by both quantifiable uncertainty and unquantifiable structural uncertainty. Thorough scientific assessment of sea-level rise projections requires analysis of both dimensions of uncertainty. Probabilistic sea-level rise projections evaluate the quantifiable dimension of uncertainty; comparison of alternative probabilistic methods provides an indication of structural uncertainty. Here we describe the Framework for Assessing Changes To Sea-level (FACTS), a modular platform for characterizing different probability distributions for the drivers of sea-level change and their consequences for global mean, regional, and extreme sea-level change. We demonstrate its application by generating seven alternative probability distributions under multiple emissions scenarios for both future global mean sea-level change and future relative and extreme sea-level change at New York City. These distributions, closely aligned with those presented in the Intergovernmental Panel on Climate Change Sixth Assessment Report, emphasize the role of the Antarctic and Greenland ice sheets as drivers of structural uncertainty in sea-level change projections.</p