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General General

A Synthetic Literature Review on the Management of Emerging Treatment Resistance in First Episode Psychosis: Can We Move towards Precision Intervention and Individualised Care?

In Medicina (Kaunas, Lithuania)

Treatment resistance is prevalent in early intervention in psychosis services, and causes a significant burden for the individual. A wide range of variables are shown to contribute to treatment resistance in first episode psychosis (FEP). Heterogeneity in illness course and the complex, multidimensional nature of the concept of recovery calls for an evidence base to better inform practice at an individual level. Current gold standard treatments, adopting a 'one-size fits all' approach, may not be addressing the needs of many individuals. This following review will provide an update and critical appraisal of current clinical practices and methodological approaches for understanding, identifying, and managing early treatment resistance in early psychosis. Potential new treatments along with new avenues for research will be discussed. Finally, we will discuss and critique the application and translation of machine learning approaches to aid progression in this area. The move towards 'big data' and machine learning holds some prospect for stratifying intervention-based subgroups of individuals. Moving forward, better recognition of early treatment resistance is needed, along with greater sophistication and precision in predicting outcomes, so that effective evidence-based treatments can be appropriately tailored to the individual. Understanding the antecedents and the early trajectory of one's illness may also be key to understanding the factors that drive illness course.

Griffiths Siân Lowri, Birchwood Max


CBT, clozapine, early intervention, first episode psychosis, psychosis, treatment resistance

Surgery Surgery

Lung transplantation for patients with severe COVID-19.

In Science translational medicine ; h5-index 138.0

Lung transplantation can potentially be a life-saving treatment for patients with non-resolving COVID-19-associated respiratory failure. Concerns limiting lung transplantation include recurrence of SARS-CoV-2 infection in the allograft, technical challenges imposed by viral-mediated injury to the native lung, and the potential risk for allograft infection by pathogens causing ventilator-associated pneumonia in the native lung. Importantly, the native lung might recover, resulting in long-term outcomes preferable to those of transplant. Here, we report the results of lung transplantation in three patients with non-resolving COVID-19-associated respiratory failure. We performed single molecule fluorescent in situ hybridization (smFISH) to detect both positive and negative strands of SARS-CoV-2 RNA in explanted lung tissue from the three patients and in additional control lung tissue samples. We conducted extracellular matrix imaging and single cell RNA sequencing on explanted lung tissue from the three patients who underwent transplantation and on warm post-mortem lung biopsies from two patients who had died from COVID-19-associated pneumonia. Lungs from these five patients with prolonged COVID-19 disease were free of SARS-CoV-2 as detected by smFISH, but pathology showed extensive evidence of injury and fibrosis that resembled end-stage pulmonary fibrosis. Using machine learning, we compared single cell RNA sequencing data from the lungs of patients with late stage COVID-19 to that from the lungs of patients with pulmonary fibrosis and identified similarities in gene expression across cell lineages. Our findings suggest that some patients with severe COVID-19 develop fibrotic lung disease for which lung transplantation is their only option for survival.

Bharat Ankit, Querrey Melissa, Markov Nikolay S, Kim Samuel, Kurihara Chitaru, Garza-Castillon Rafael, Manerikar Adwaiy, Shilatifard Ali, Tomic Rade, Politanska Yuliya, Abdala-Valencia Hiam, Yeldandi Anjana V, Lomasney Jon W, Misharin Alexander V, Budinger G R Scott


General General

An enhanced Random Forests approach to predict heart failure from small imbalanced gene expression data.

In IEEE/ACM transactions on computational biology and bioinformatics

Myocardial infarctions and heart failure are the cause of more than 17 million deaths annually worldwide. ST-segment elevation myocardial infarctions (STEMI) require timely treatment, because delays of minutes have serious clinical impacts. Machine learning can provide alternative ways to predict heart failure and identify genes invovled in heart failure. For these scopes, we applied a Random Forests classifier enhanced with feature elimination to microarray gene expression of 111 patients diagnosed with STEMI, and measured the classification performance through standard metrics such as the Matthews correlation coefficient (MCC) and area under the receiver operating characteristic curve (ROC AUC) Afterwards, we used the same approach to rank all genes by importance, and to detect the genes more strongly associated with heart failure. We validated this ranking by literature review and gene set enrichment analysis. Our classifier achieved MCC = +0.87 and ROC AUC = 0.918, and our analysis identified KLHL22, WDR11, OR4Q3, GPATCH3, and FAH as top five protein-coding genes related to heart failure. Our results confirm the effectiveness of machine learning feature elimination in predicting heart failure from gene expression, and the top genes found by our approach will be able to help biologists and cardiologists further our understanding of heart failure.

Chicco Davide, Oneto Luca


General General

Deep Exemplar-based Color Transfer for 3D Model.

In IEEE transactions on visualization and computer graphics

Recoloring 3D models is a challenging task that often requires professional knowledge and tedious manual efforts. In this paper, we present the first deep-learning framework for exemplar-based 3D model recolor, which can automatically transfer the colors from a reference image to the 3D model texture. Our framework consists of two modules to solve two major challenges in the 3D color transfer. First, we propose a new feed-forward Color Transfer Network to achieve high-quality semantic-level color transfer by finding dense semantic correspondences between images. Second, considering 3D model constraints such as UV mapping, we design a novel 3D Texture Optimization Module which can generate a seamless and coherent texture by combining color transferred results rendered in multiple views. Experiments show that our method performs robustly and generalizes well to various kinds of models.

Zhang Mohan, Liao Jing, Yu Jinhui


General General

Outcome-Oriented Deep Temporal Phenotyping of Disease Progression.

In IEEE transactions on bio-medical engineering

Chronic diseases evolve slowly throughout a patient's lifetime creating heterogeneous progression patterns that make clinical outcomes remarkably varied across individual patients. A tool capable of identifying temporal phenotypes based on the patients' different progression patterns and clinical outcomes would allow clinicians to better forecast disease progression by recognizing a group of similar past patients, and to better design treatment guidelines that are tailored to specific phenotypes. To build such a tool, we propose a deep learning approach, which we refer to as outcome-oriented deep temporal phenotyping (ODTP), to identify temporal phenotypes of disease progression considering what type of clinical outcomes will occur and when based on the longitudinal observations. More specifically, we model clinical outcomes throughout a patient's longitudinal observations via time-to-event (TTE) processes whose conditional intensity functions are estimated as non-linear functions using a recurrent neural network. Temporal phenotyping of disease progression is carried out by our novel loss function that is specifically designed to learn discrete latent representations that best characterize the underlying TTE processes. The key insight here is that learning such discrete representations groups progression patterns considering the similarity in expected clinical outcomes, and thus naturally provides outcome-oriented temporal phenotypes. We demonstrate the power of ODTP by applying it to a real-world heterogeneous cohort of 11,779 stage III breast cancer patients from the UK National Cancer Registration and Analysis Service. The experiments show that ODTP identifies temporal phenotypes that are strongly associated with the future clinical outcomes and achieves significant gain on the homogeneity and heterogeneity measures over existing methods. Furthermore, we are able to identify the key driving factors that lead to transitions between phenotypes which can be translated into actionable information to support better clinical decision-making.

Lee Changhee, Rashbass Jem, Van Der Schaar Mihaela


Public Health Public Health

Reusable Self-Sterilization Masks Based on Electrothermal Graphene Filters.

In ACS applied materials & interfaces ; h5-index 147.0

Surgical mask is recommended by the World Health Organization for personal protection against disease transmission. However, most of the surgical masks on the market are disposable that cannot be self-sterilized for reuse. Thus, when confronting the global public health crisis, a severe shortage of mask resource is inevitable. In this paper, a novel low-cost electrothermal mask with excellent self-sterilization performance and portability is reported to overcome this shortage. First, a flexible, ventilated, and conductive cloth tape is patterned and adhered to the surface of a filter layer made of melt-blown nonwoven fabrics (MNF), which functions as interdigital electrodes. Then, a graphene layer with premier electric and thermal conductivity is coated onto the MNF. Operating under a low voltage of 3 V, the graphene-modified MNF (mod-MNF) can quickly generate large amounts of heat to achieve a high temperature above 80 °C, which can kill the majority of known viruses attached to the filter layer and the mask surface. Finally, the optimized graphene-modified masks based on the mod-MNF filter retain a relatively high particulate matter (PM) removal efficiency and a low-pressure drop. Moreover, the electrothermal masks can maintain almost the same PM removal efficiency over 10 times of electrifying, suggesting its outstanding reusability.

Shan Xiaoli, Zhang Han, Liu Cihui, Yu Liyan, Di Yunsong, Zhang Xiaowei, Dong Lifeng, Gan Zhixing


COVID-19, electrothermal effect, graphene ink, self-sterilization, surgical masks