CHARD: Clinical Health-Aware Reasoning Across Dimensions for Text Generation Models

We motivate and introduce CHARD: Clinical Health-Aware Reasoning across Dimensions, to investigate the capability of text generation models to act as implicit clinical knowledge bases and generate free-flow textual explanations about various health-related conditions across several dimensions. We collect and present an associated dataset, CHARDat, consisting of explanations about 52 health conditions across three clinical dimensions. We conduct extensive experiments using BART and T5 along with data augmentation, and perform automatic, human, and qualitative analyses. We show that while our models can perform decently, CHARD is very challenging with strong potential for further exploration

A kinetic model of TBP auto-regulation exhibits bistability

<p>Abstract</p> <p>Background</p> <p>TATA Binding Protein (TBP) is required for transcription initiation by all three eukaryotic RNA polymerases. It participates in transcriptional initiation at the majority of eukaryotic gene promoters, either by direct association to the TATA box upstream of the transcription start site or by indirectly localizing to the promoter through other proteins. TBP exists in solution in a dimeric form but binds to DNA as a monomer. Here, we present the first mathematical model for auto-catalytic TBP expression and use it to study the role of dimerization in maintaining the steady state TBP level.</p> <p>Results</p> <p>We show that the autogenous regulation of TBP results in a system that is capable of exhibiting three steady states: an unstable low TBP state, one stable state corresponding to a physiological TBP concentration, and another stable steady state corresponding to unviable cells where no TBP is expressed. Our model predicts that a basal level of TBP is required to establish the transcription of the TBP gene, and hence for cell viability. It also predicts that, for the condition corresponding to a typical mammalian cell, the high-TBP state and cell viability is sensitive to variation in DNA binding strength. We use the model to explore the effect of the dimer in buffering the response to changes in TBP levels, and show that for some physiological conditions the dimer is not important in buffering against perturbations.</p> <p>Conclusions</p> <p>Results on the necessity of a minimum basal TBP level support the in vivo observations that TBP is maternally inherited, providing the small amount of TBP required to establish its ubiquitous expression. The model shows that the system is sensitive to variations in parameters indicating that it is vulnerable to mutations in TBP. A reduction in TBP-DNA binding constant can lead the system to a regime where the unviable state is the only steady state. Contrary to the current hypotheses, we show that under some physiological conditions the dimer is not very important in restoring the system to steady state. This model demonstrates the use of mathematical modelling to investigate system behaviour and generate hypotheses governing the dynamics of such nonlinear biological systems.</p> <p>Reviewers</p> <p>This article was reviewed by Tomasz Lipniacki, James Faeder and Anna Marciniak-Czochra.</p

Double rolling and center hitch technique for laparoscopic ventral hernia repair

Background: Intraperitoneal onlay mesh repair is an established modality to treat large ventral hernias. Various techniques of laying the mesh are utilized. We present the Double Rolling and Center Hitch technique to lay a large intraperitoneal onlay mesh. Objective: The aim of the study is to devise and adopt a method to reduce the difficulty in manoeuvring a large mesh inside the peritoneal cavity. It should also help in correct placement of mesh and decrease the operative time. Materials and Methods: The DRACH technique was used in eighteen patients with large ventral hernias between May 2010 and September 2011. The Mesh size used was 15x20cm and more (considered to be large mesh). Results: All the procedures were completed successfully. Mesh handling was significantly easier with the DRACH technique. The average mesh deployment time (MDT) was 15mins. In all cases the mesh was adequately centred with a margin of 3-5cm from the defect. Conclusion: The DRACH technique can be employed to lay large intraperitoneal meshes in order to reduce the handling difficulties associated with large meshes, and to aid in better placement of meshes so as to centered over the defect

Representation Projection Invariance Mitigates Representation Collapse

Fine-tuning contextualized representations learned by pre-trained language models remains a prevalent practice in NLP. However, fine-tuning can lead to representation degradation (also known as representation collapse), which may result in instability, sub-optimal performance, and weak generalization. In this paper, we propose Representation Projection Invariance (REPINA), a novel regularization method to maintain the information content of representation and reduce representation collapse during fine-tuning by discouraging undesirable changes in the representations. We study the empirical behavior of the proposed regularization in comparison to 5 comparable baselines across 13 language understanding tasks (GLUE benchmark and six additional datasets). When evaluating in-domain performance, REPINA consistently outperforms other baselines on most tasks (10 out of 13). We also demonstrate its effectiveness in few-shot settings and robustness to label perturbation. As a by-product, we extend previous studies of representation collapse and propose several metrics to quantify it. Our empirical findings show that our approach is significantly more effective at mitigating representation collapse.Comment: 41 pages, 6 figure

An uncommon cause of rapidly progressive renal failure in a lupus patient: Pauci-immune crescentic glomerulonephritis

We report a case of systemic lupus erythematosus (SLE) who presented with rapidly progressive renal failure (RPRF) with positive antinuclear antibody (ANA) and anti-double-stranded DNA (dsDNA) antibody and active urinary sediment in the form of microscopic hematuria and proteinuria. Provisional clinical diagnosis of lupus nephritis was made. Renal biopsy showed pauci-immune crescentic glomerulonephritis, the diagnosis of which was supported by positive serum anti-MPO antibody. Renal biopsy in SLE patients can sometimes reveal varied pathological entities such as antinuclear cytoplasmic antibodies (ANCAs) positive vasculitis, as in our case, which modified our treatment protocol. Thus, in a patient with SLE presenting with RPRF with active urinary sediments, ANCA serology, and renal biopsy with immunofluorescence examination should be performed always

Review of contemporary role of robotics in bariatric surgery

With the rise in a number of bariatric procedures, surgeons are facing more complex and technically demanding surgical situations. Robotic digital platforms potentially provide a solution to better address these challenges. This review examines the published literature on the outcomes and complications of bariatric surgery using a robotic platform. Use of robotics to perform adjustable gastric banding, sleeve gastrectomy, roux-en-y gastric bypass (RYGB), biliopancreatic diversion with duodenal switch and revisional bariatric procedures (RBP) is assessed. A search on PubMed was performed for the most relevant articles in robotic bariatric surgery. A total of 23 articles was selected and reviewed in this article. The review showed that the use of robotics led to similar or lower complication rate in bariatric surgery when compared with laparoscopy. Two studies found a significantly lower leak rate for robotic gastric bypass when compared to laparoscopic method. The learning curve for RYGB seems to be shorter for robotic technique. Three studies revealed a significantly shorter operative time, while four studies found a longer operative time for robotic technique of gastric bypass. As for the outcomes of RBP, one study found a lower complication rate in robotic arm versus laparoscopic and open arms. Most authors stated that the use of robotics provides superior visualisation, more degrees of freedom and better ergonomics. The application of robotics in bariatric surgery seems to be a safe and feasible option. Use of robotics may provide specific advantages in some situations, and overcome limitations of laparoscopic surgery. Large and well-designed randomised clinical trials with long follow-up are needed to further define the role of digital platforms in bariatric surgery

Extraction of Explicit and Implicit Cause-Effect Relationships in Patient-Reported Diabetes-Related Tweets From 2017 to 2021: Deep Learning Approach

Background Intervening in and preventing diabetes distress requires an understanding of its causes and, in particular, from a patient’s perspective. Social media data provide direct access to how patients see and understand their disease and consequently show the causes of diabetes distress. Objective Leveraging machine learning methods, we aim to extract both explicit and implicit cause-effect relationships in patient-reported diabetes-related tweets and provide a methodology to better understand the opinions, feelings, and observations shared within the diabetes online community from a causality perspective. Methods More than 30 million diabetes-related tweets in English were collected between April 2017 and January 2021. Deep learning and natural language processing methods were applied to focus on tweets with personal and emotional content. A cause-effect tweet data set was manually labeled and used to train (1) a fine-tuned BERTweet model to detect causal sentences containing a causal relation and (2) a conditional random field model with Bidirectional Encoder Representations from Transformers (BERT)-based features to extract possible cause-effect associations. Causes and effects were clustered in a semisupervised approach and visualized in an interactive cause-effect network. Results Causal sentences were detected with a recall of 68% in an imbalanced data set. A conditional random field model with BERT-based features outperformed a fine-tuned BERT model for cause-effect detection with a macro recall of 68%. This led to 96,676 sentences with cause-effect relationships. “Diabetes” was identified as the central cluster followed by “death” and “insulin.” Insulin pricing–related causes were frequently associated with death. Conclusions A novel methodology was developed to detect causal sentences and identify both explicit and implicit, single and multiword cause, and the corresponding effect, as expressed in diabetes-related tweets leveraging BERT-based architectures and visualized as cause-effect network. Extracting causal associations in real life, patient-reported outcomes in social media data provide a useful complementary source of information in diabetes research