Plain Background Research Articles

Towards robust crop disease detection for complex real field background images

Most of the work done in image processing-based crop disease detection focuses on images with plain background. This paper presents a technique for crop disease detection for complex real field background images. A segmentation technique is presented to extract leaf patches from the entire image. Transform domain cepstral analysis is proposed for obtaining cepstral coefficients, to attain two level classifications. The first level classifies the crop species while the second level classifies the species into healthy leaf or leaf with specific type of disease. The work is tested on three crops Banana, Soybean and Grape and is checked on plain background laboratory images and on complex real field images. Suggested technique give species level accuracy of 94.33 %, 94.11 % and 98.44 % and disease level average accuracy of 97.75 %, 96.66 % and 97.95 % for Banana, Soybean and Grape, respectively. Comparison with standard features like texture and shape indicate that the presented technique gives the best results for both plain and complex background images suggesting its utilization in crop disease detection to reduce the agricultural and economic losses.

Judging emotion in natural images of crowds.

It has been suggested that humans use summary statistics such as the average of the emotion of individual faces when they rapidly judge group emotion. Previous studies have mainly used faces of actors posing basic emotions, and morphed versions of these faces, against a plain background. In the present study, photographs taken in real-world settings were used to investigate the influence of mean facial emotion, maximal facial emotion, and background context on judgments of group emotion, assessed using dimensional ratings of valence, arousal, and dominance. Background context explained a significant amount of unique variance in group ratings for each dimension. Mean emotion explained additional unique variance for valence ratings, whereas maximal emotion explained additional unique variance for arousal, with dominance showing more mixed results. Removing background context and disrupting the contextual and spatial relationship between faces by randomly replacing faces with ones from other images within the stimulus set increased reliance on mean emotion. However, under all conditions, the maximally arousing face continued to exert an influence on ratings of group arousal, in line with theoretical accounts arguing for a unique bottom-up effect of emotional arousal on attentional competition and postattentive perceptual processing. Together these findings suggest that individuals' reliance on average emotion when judging crowd scenes differs as a function of the dimension of affect. In addition, the presence of background context both directly impacts judgments of crowd emotion and modulates the relative influence of maximal versus mean emotion on these judgments. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

An automated method for the generation of bloodstain pattern metrics from images of blood spatter patterns

An improved automated bloodstain pattern analysis method has been developed and validated, which utilises computer vision techniques to identify bloodstains on a plain background within a digital image. The method generates metrics relating to the individual stains as well as the overall pattern, including bloodstain pattern specific metrics such as the gamma angle, circularity, solidity, area of convergence, stain density and pattern linearity. This method provides an objective approach to the analysis of bloodstains and bloodstain patterns and can generate a wealth of quantitative data that is currently not obtainable using manual techniques or other image-based programs currently utilised in the discipline. This method will be useful to analysts and researchers investigating the application of quantitative methods to bloodstain pattern analysis.

Image restoration for digital line drawings using line masks

The restoration of digital images holds practical significance due to the fact that degradation of digital image data on the internet is common. State-of-the-art image restoration methods usually employ end-to-end trained networks. However, we argue that a network trained with diverse image pairs is not optimal for restoring line drawings which have extensive plain backgrounds. We propose a line-drawing restoration framework which takes a restoration neural network as backbone and processes an input degraded line drawing in two steps. First, a proposed mask-predicting network predicts a line mask which indicates the possible location of foreground and background in the potential original line drawing. Next, we feed the degraded input line drawing together with the predicted line mask into the backbone restoration network. The traditional L1 loss for the backbone restoration network is substituted with a masked Mean Square Error (MSE) loss. We test our framework on two classical image restoration tasks: JPEG restoration and super-resolution, and experiments demonstrate that our framework can achieve better quantitative and visual results in most cases.

Are virtual backgrounds during online learning distracting? An attention study using eye tracker technology

During and after the Covid-19 pandemic, online learning has gained popularity. Video conferencing, such as Zoom, has become the new normal in almost every individual’s life. The purpose of this study is to observe the usage and preference of Virtual Background (VB) during video conferencing through a survey, whereas the effect of VB on attention during online meetings is evaluated through an eye tracker experiment. An online survey is conducted to observe the usage and preference of Indonesian users of Zoom in applied VB. Based on the most preferred VB, an eye tracker experiment was conducted to evaluate participants’ attention during online learning using the three most preferred VB: office VB, plain background, and thematic VB. Eye tracker parameters, including Percentage of Participants who Fixated (PFF), Total Fixation Duration (TFD), Time to First Fixation (TFF), and Fixation Count (FC), were measured. The survey result showed that 71% of 303 respondents preferred their lecturers to use VB during Zoom meetings, 26% preferred office VB, 25% preferred plain VB, and 20% preferred thematic VB. An experiment that is conducted to evaluate attention during online meetings with three different VBs using an eye tracker shows that attention to lecture in plain VB is the best compared to other VBs, shown by the highest value of TFF and lowest TFD and FC. Implications of the result are discussed.

Classifying stages in the gonotrophic cycle of mosquitoes from images using computer vision techniques

The ability to distinguish between the abdominal conditions of adult female mosquitoes has important utility for the surveillance and control of mosquito-borne diseases. However, doing so requires entomological training and time-consuming manual effort. Here, we design computer vision techniques to determine stages in the gonotrophic cycle of female mosquitoes from images. Our dataset was collected from 139 adult female mosquitoes across three medically important species—Aedes aegypti, Anopheles stephensi, and Culex quinquefasciatus—and all four gonotrophic stages of the cycle (unfed, fully fed, semi-gravid, and gravid). From these mosquitoes and stages, a total of 1959 images were captured on a plain background via multiple smartphones. Subsequently, we trained four distinct AI model architectures (ResNet50, MobileNetV2, EfficientNet-B0, and ConvNeXtTiny), validated them using unseen data, and compared their overall classification accuracies. Additionally, we analyzed t-SNE plots to visualize the formation of decision boundaries in a lower-dimensional space. Notably, ResNet50 and EfficientNet-B0 demonstrated outstanding performance with an overall accuracy of 97.44% and 93.59%, respectively. EfficientNet-B0 demonstrated the best overall performance considering computational efficiency, model size, training speed, and t-SNE decision boundaries. We also assessed the explainability of this EfficientNet-B0 model, by implementing Grad-CAMs—a technique that highlights pixels in an image that were prioritized for classification. We observed that the highest weight was for those pixels representing the mosquito abdomen, demonstrating that our AI model has indeed learned correctly. Our work has significant practical impact. First, image datasets for gonotrophic stages of mosquitoes are not yet available. Second, our algorithms can be integrated with existing citizen science platforms that enable the public to record and upload biological observations. With such integration, our algorithms will enable the public to contribute to mosquito surveillance and gonotrophic stage identification. Finally, we are aware of work today that uses computer vision techniques for automated mosquito species identification, and our algorithms in this paper can augment these efforts by enabling the automated detection of gonotrophic stages of mosquitoes as well.

A Seven-Step Technique on PowerPoint Slide Preparation for Anatomy Lectures: A Cognitive Load Theory Approach

Lecturing remains a main teaching method in anatomy education, despite being argued as ineffective in promoting understanding and higher-order cognitive skills. Designing and preparing anatomy lecture slides is a challenging task, as anatomy is a content-driven subject that involves complex threedimensional diagrams. Overly wordy, overcrowded slides and poor organisation of text and pictorial materials are among the problems related to anatomy lecture slide preparation. These unorganised and problematic slides impose a high cognitive load on students and thus hinder learning. Hence, this paper elaborates on the strategies for lecture slide preparation that were formerly mentioned by the authors in a lecturing guideline known as the cognitive load theory lecture model. The strategies were organised into seven steps: (a) step 1: select plain background slides; (b) step 2: use headings and subheadings; (c) step 3: use visual cues and signals; (d) step 4: select a proper font type and size; (e) step 5: use text treatment; (f) step 6: choose a suitable colour scheme; and (g) step 7: synchronise all sub-elements. This paper also provides tested examples for each step to facilitate comprehension of the lecture content while applying the strategies.

Convolutional Neural Networks for Herb Identification: Plain Background and Natural Environment

Convolutional neural networks have achieved success in resolving object identification problems. This study contributes a suitable new approach to herb identification for educational and research purposes based on a small dataset and small-sized images. Two self-collected Thai herb datasets with either plain or natural environment backgrounds were used for experimentation to realize this objective. The plain background dataset includes 4,400 images of 11 leaf types, and the natural dataset contains 1,620 images of nine leaf types. The images were divided into a training set containing 75% of the images and a separate test set with the remaining 25%. The experiments included five-fold cross-validation applied to the training set; the InceptionV3, MobileNetV2, ResNet50V2, VGG16, and Xception convolutional neural network models RMSprop and Adam optimizers. Further, dropout rates of 0.3, 0.5, and 0.7 were considered along with five and ten epochs. Transfer learning was applied using pre-trained weights. The model with the best outcome, based on the average accuracy of the cross-validation results on both datasets (the plain background dataset was 94.55%, and the natural dataset was 90.37%), was the VGG16 with the RMSprop optimizer, which exhibited a dropout rate of 0.5 over ten epochs. The model achieved 96.64% and 92.00% accuracy on the plain background training and test sets, and 99.59% and 91.36% on the natural environment training and test sets, respectively. The results show that the method has a high potential for objective tasks and application in identifying herbs based on visual leaf information.

Rational use of antibiotics and covariates of clinical outcomes in patients admitted to intensive care units of a tertiary hospital in Kenya

Objectives : Rational use of antibiotics implies appropriate choice of an antibiotic administered at correct dose, frequency and duration using the most suitable route of administration. Irrational antibiotics use is associated with antimicrobial resistance, drug failure and high mortality in the critical care units (CCUs). This study sought to establish rational use of antibiotics and determinants of clinical outcomes of patients admitted in the CCUs at the Kenyatta National Hospital (KNH). The findings would guide policy formulation of antibiotics use in hospital CCUs in Kenya and the region. Methods : Retrospective review of 220 admissions to the KNH CCUs over the period February 2018–February 2020 was conducted. Participants’ sociodemographics, clinical characteristics, antibiotics therapy, and outcome of admission were extracted from patient files and analyzed using STATA version 23. Determinants of irrational antibiotic use and covariates of clinical outcomes were computed at 95% confidence. Results The prevalence of rational use of antibiotics was only 18.5%. Inappropriate choice of antibiotics (51.0%) and incorrect duration (32.3%) were the most common irrational practices. Flucloxacillin (100%), cefuroxime (93.3%), cefazolin (85.7%) and ceftriaxone (83.0%) were the most irrationally used antibiotics. Irrational use of ceftriaxone was significantly associated with clinical diagnosis (p=0.012) while that of amoxiclav was associated with patient risk category (p=0.039). Mortality in the CCUs was 10%, and the odds of dying were almost six times among intubated patients compared to those who were not (AOR 5.5, 95% CI=1.1–28.1, p=0.042). Conclusion Irrational antibiotics prescribing is high in the KNH CCUs, attributable largely to incorrect choice and wrong duration of antibiotic use. Mortality was significantly associated with intubation. Intensification of management in critical care settings should be directed towards intubated patients while ensuring appropriate choice of antibiotics administered for the correct duration. Future studies should explore factors that could promote rational antibiotics use in critical care settings.

Shortcomings in Public Health Authorities’ Videos on COVID-19: Limited Reach and a Creative Gap

Video communication has played a key role in relaying important and complex information on the COVID-19 pandemic to the general public. The aim of the present study is to compare Norwegian health authorities’ and WHO’s use of video communication during the COVID-19 pandemic to the most viewed COVID-19 videos on YouTube, in order to identify how videos created by health authorities measure up to contemporary video content, both creatively and in reaching video consumers. Through structured search on YouTube we found that Norwegian health authorities have published 26 videos, and the WHO 29 videos on the platform. Press briefings, live videos, news reports, and videos recreated/translated into other languages than English or Norwegian, were not included. A content analysis comparing the 55 videos by the health authorities to the 27 most viewed videos on COVID-19 on YouTube demonstrates poor reach of health authorities’ videos in terms of views and it elucidates a clear creative gap. While the videos created by various YouTube creators communicate using a wide range of creative presentation means (such as professional presenters, contextual backgrounds, advanced graphic animations, and humour), videos created by the health authorities are significantly more homogenous in style often using field experts or public figures, plain backgrounds or PowerPoint style animations. We suggest that further studies into various creative presentation means and their influence on reach, recall, and on different groups of the population, are carried out in the future to evaluate specific factors of this creative gap.

PLRSNet: a semantic segmentation network for segmenting plant leaf region under complex background

PurposeExtraction of leaf region from the plant leaf images is a prerequisite process for species recognition, disease detection and classification and so on, which are required for crop management. Several approaches were developed to implement the process of leaf region segmentation from the background. However, most of the methods were applied to the images taken under laboratory setups or plain background, but the application of leaf segmentation methods is vital to be used on real-time cultivation field images that contain complex backgrounds. So far, the efficient method that automatically segments leaf region from the complex background exclusively for black gram plant leaf images has not been developed.Design/methodology/approachExtracting leaf regions from the complex background is cumbersome, and the proposed PLRSNet (Plant Leaf Region Segmentation Net) is one of the solutions to this problem. In this paper, a customized deep network is designed and applied to extract leaf regions from the images taken from cultivation fields.FindingsThe proposed PLRSNet compared with the state-of-the-art methods and the experimental results evident that proposed PLRSNet yields 96.9% of Similarity Index/Dice, 94.2% of Jaccard/IoU, 98.55% of Correct Detection Ratio, Total Segmentation Error of 0.059 and Average Surface Distance of 3.037, representing a significant improvement over existing methods particularly taking into account of cultivation field images.Originality/valueIn this work, a customized deep learning network is designed for segmenting plant leaf region under complex background and named it as a PLRSNet.

Effects of nature virtual backgrounds on creativity during videoconferencing

Previous research has demonstrated that exposure to nature environments versus urban environments can help restore cognitive processing and improve overall well-being. The current study examined the restorative effects of nature on creativity using virtual backgrounds during videoconferencing on Zoom. Eighty participants completed the Alternative Uses Test (AUT) of creative divergent thinking when viewing one of three different virtual background images: nature setting, urban setting, plain grey background (control). Results showed that the nature background facilitated higher creativity performance compared to either the urban or control backgrounds (which did not significantly differ from each other). The results are discussed in terms of practical implications for workplace and educational environments that involve videoconferencing sessions between users.

Customised vestibular rehabilitation with the addition of virtual reality based therapy in the management of persistent postural-perceptual dizziness.

Visual-vestibular mismatch patients experience persistent postural and perceptual dizziness. Previous studies have shown the benefit of vestibular rehabilitation for visual desensitisation using gaze stabilisation exercises and optokinetic stimulation. This study assessed the benefit of customised vestibular rehabilitation with visual desensitisation and virtual reality based therapy rehabilitation in the management of patients with persistent postural-perceptual dizziness. This retrospective study included 100 patients with Situational Characteristic Questionnaire scores of more than 0.9. All patients received virtual reality based therapy along with usual vestibular rehabilitation using gaze stabilisation exercises with a plain background followed by graded visual stimulation and optokinetic digital video disc stimulation. Patients' symptoms were assessed before and after vestibular rehabilitation using the Situational Characteristic Questionnaire, Generalised Anxiety Disorder Assessment-7, Nijmegen Questionnaire and Dizziness Handicap Inventory. There were statistically significant improvements in Situational Characteristic Questionnaire scores, Nijmegen Questionnaire scores and Dizziness Handicap Inventory total score. However, there was a statistically insignificant difference in Generalised Anxiety Disorder Assessment-7 scores. There was a significant positive correlation between post-rehabilitation Situational Characteristic Questionnaire scores and other questionnaire results. Incorporating virtual reality based therapy with customised vestibular rehabilitation exercises results in significant improvement in persistent postural-perceptual dizziness related symptoms.

Credit scoring by one-class classification driven dynamical ensemble learning

It is very useful to endow machines with the ability to measure credit scores of loan applicants. Conventional methodologies always train Credit Scoring (CS) models by using data from clients who passed previous credit examination (i.e. who were considered adequately creditworthy and took out a loan). However, the CS models trained on data from the applicants who with good credit background may not work well for new applicants with plain or ambiguous credit backgrounds. Previous work always alleviates this by techniques of rejected inference and semisupervised learning. In this article, we propose a novel approach called as “One-class Classification Driven Dynamical Ensemble Learning” (abbreviated as OCDDEL). Different from rejected inference or semisupervised learning, OCDDEL does not use inferred labels of past rejected applications. Instead, OCDDEL only relies on past accepted applications and their true labels. It builds a dynamical ensemble model which deal with different test applications in different ways. To determine the ensemble weights for a specific test case, OCDDEL will learn a one-class classifier to separate test applications into groups, according to their similarities with training applicants. An experimental evaluation with 2 real-world datasets demonstrates the effectiveness of our approach.

Variation-set structure: Extraction of important segments in pigeon learning

The ability to extract reliable segments from the sensory data-stream is necessary for learning and for creating a reliable representation of the environment. Here we focused on segmentation in visual learning associated with foraging. Fifty-one pigeons (Columba livia) were trained to recognize two food-predicting signals, one presented against fixed background elements and the other against varying backgrounds. The pigeons were divided into two groups: 32 pigeons were trained with short intervals of 3−5 min between sessions and 19 with long intervals of 60 min between sessions. When tested on the signal with a plain background, 24 pigeons trained with the short time intervals preferred the signal presented against a varying background during training and eight preferred the signal presented against a fixed background during training. Nine pigeons trained with the long intervals preferred the signal presented against a varying background during training and ten the signal presented against a fixed background during training.Our findings indicate that pigeons possess an ability to use variation-set structure to segment visual data. To date, the effect of variation-set structure has been demonstrated only in the context of human language acquisition. Our current findings suggest that variation sets structure may be linked to other learning abilities too.

Dynamic tree branch tracking for aerial canopy sampling using stereo vision

Maintaining the massive forests of the forestry industry requires physical samples from the tree canopy to enable breeding, genetics research and monitoring of diseases. An autonomous Unmanned Aerial Vehicle (UAV) canopy sampling solution requires locating and tracking a highly unstructured, moving object against a dynamic, cluttered background. The developed algorithm utilises stereo vision techniques to reconstruct a 3D point cloud of the target branch and subsequently estimate the intercept point location. The optimal parameters are selected by quantifying the accuracy against ground truth obtained from motion capture. Since wind will likely affect the tree canopy during sampling, the branch will exhibit some sway motion which can be modelled as simple harmonic motion. Hence, a linear Kalman filter is formulated to estimate the sway parameters. With the branch 1m from the camera, the average error is 41mm for sway amplitudes between 50mm and 250mm with a plain background. When tested with a cluttered background, the reduced contrast results in higher standard deviations. Overall, the developed Kalman filter significantly reduces the intercept point error standard deviation, averaging 17% and 33% reductions for plain and cluttered backgrounds respectively. Subsequent outdoor testing verified the capability of the developed algorithm to estimate the branch trajectory to well within the maximum gripper opening of 115mm.

Deep Learning Using Isotroping, Laplacing, Eigenvalues Interpolative Binding, and Convolved Determinants with Normed Mapping for Large-Scale Image Retrieval.

Convolutional neural networks (CNN) are relational with grid-structures and spatial dependencies for two-dimensional images to exploit location adjacencies, color values, and hidden patterns. Convolutional neural networks use sparse connections at high-level sensitivity with layered connection complying indiscriminative disciplines with local spatial mapping footprints. This fact varies with architectural dependencies, insight inputs, number and types of layers and its fusion with derived signatures. This research focuses this gap by incorporating GoogLeNet, VGG-19, and ResNet-50 architectures with maximum response based Eigenvalues textured and convolutional Laplacian scaled object features with mapped colored channels to obtain the highest image retrieval rates over millions of images from versatile semantic groups and benchmarks. Time and computation efficient formulation of the presented model is a step forward in deep learning fusion and smart signature capsulation for innovative descriptor creation. Remarkable results on challenging benchmarks are presented with a thorough contextualization to provide insight CNN effects with anchor bindings. The presented method is tested on well-known datasets including ALOT (250), Corel-1000, Cifar-10, Corel-10000, Cifar-100, Oxford Buildings, FTVL Tropical Fruits, 17-Flowers, Fashion (15), Caltech-256, and reported outstanding performance. The presented work is compared with state-of-the-art methods and experimented over tiny, large, complex, overlay, texture, color, object, shape, mimicked, plain and occupied background, multiple objected foreground images, and marked significant accuracies.

Comprehensive Multilayer Convolutional Neural Network for Plant Disease Detection

Agriculture has a dominant role in the world’s economy. However, losses due to crop diseases and pests significantly affect the contribution made by the agricultural sector. Plant diseases and pests recognized at an early stage can help limit the economic losses in agriculture production around the world. In this paper, a comprehensive multilayer convolutional neural network (CMCNN) is developed for plant disease detection that can analyze the visible symptoms on a variety of leaf images like, laboratory images with a plain background, complex images with real field conditions and images of individual disease symptoms or spots. The model performance is evaluated on three public datasets -Plant Village repository having images of the whole leaf with plain background, Plant Village repository with complex background and Digipathos repository with images of lone lesions and spots. Hyperparameters like learning rate, dropout probability, and optimizer are fine-tuned such that the model is capable of classifying various types of input leaf images. The overall classification accuracy of the model in handling laboratory images is 99.85%, real field condition images is 98.16% and for images with individual disease symptoms is 99.6%. The proposed design is also compared with the popular CNN architectures like GoogleNet, VGG16, VGG19 and ResNet50. The experimental results indicate that the suggested generic model has higher robustness in handling various types of leaf images and has better classification capability for plant disease detection. The obtained results suggest the favorable use of the proposed model in a decision support system to identify diseases in several plant species for a large range of leaf images.

Prospective cohort study of nonspecific deprescribing in older medical inpatients being discharged to a nursing home.

Background:Older patients from nursing homes are commonly exposed to polypharmacy before a hospital admission. Deprescribing has been promoted as a solution to this problem, though systematic reviews have not found benefit. The aim of this study was to understand if in-hospital deprescribing of certain classes of medications is associated with certain benefits or risks.Methods:We conducted a prospective, multicentre, cohort study in 239 medical inpatients ⩾75 years (mean age 87.4 years) who were exposed to polypharmacy (⩾5 medications) prior to admission and discharged to a nursing home for permanent placement. Patients were categorised by whether deprescribing occurred, mortality and readmissions were assessed 30 and 90 days after hospital discharge. The EQ-5D-5 L health survey assessed changes in health-related quality of life (HRQOL) at 90 days, with comparison to EQ-5D-5 L results at day 30. Latent class analysis (LCA) was used to investigate associations between patterns of prescribed and deprescribed medications and mortality.Results:Patients for whom deprescribing occurred had a higher Charlson Index; there were no differences between the groups in principal diagnosis, total or Beers list number of medications on admission. The number of Beers list medications increased in both groups before discharge. Patients who had medications deprescribed had nonsignificantly greater odds of dying within 90 days [odds ration (OR) = 3.23 (95% confidence interval (CI): 0.68, 14.92; p = 0.136]. Deprescribing of certain classes was associated with higher 90-day mortality: antihypertensives (OR = 2.27, 95% CI: 1.004, 5; p = 0.049) and statins (OR = 5, 95% CI: 1.61, 14.28; p = 0.005). Readmissions and 1-year mortality rates were similar. There was no deterioration in HRQOL when medications were deprescribed. LCA showed that patients with the least medication changes had the lowest mortality.Conclusion:Deprescribing certain classes of medications during hospitalisation was associated with worse mortality, but not readmissions or overall HRQOL. Larger controlled deprescribing studies targeting specific medications are warranted to further investigate these findings.This study was registered with the Australian and New Zealand Clinical Trials Registry, ACTRN1 2616001336471.Plain language summaryBackground: When an older person living in a nursing home is admitted to hospital, does stopping long-term medications help them?Many older people from nursing homes take a large number of medications each day to treat symptoms and prevent adverse events. “Polypharmacy” is a term used to describe taking multiple long-term medications, and it is associated with many negative outcomes such as increased number of falls, cognitive decline, hospital readmission, even death. Deprescribing of nonessential medications – whether stopping or reducing the dose – is promoted as good hospital practice and is assumed to help older frail people live longer and feel better. However, we often don’t fully understand what is and is not essential.We wanted to better understand the effect of deprescribing long-term medications for older frail patients during an unplanned hospital admission as they were going to a nursing home to live.Methods: While admitted to hospital, medications are often reviewed by a clinical pharmacist and specialist physician. Sometimes medications are ceased; sometimes they are not. This gave us the opportunity to study two groups of older frail people from nursing homes: those who had regular, long-term medications ceased or reduced and those who did not. We wanted to see if one group did better. For example, did they feel worse if we stopped certain medications? Did they suffer other bad events compared with those patients for whom no medications were ceased? Were they readmitted to hospital earlier or more often?Results and conclusion: Despite the assumption that stopping medications for this type of patient is good practice, we found no benefit. We were also surprised to find stopping or reducing certain drug classes (e.g. antihypertensives and cholesterol-lowering drugs) was associated with greater mortality. Larger, randomised studies will better answer these important questions.

Applying to Residency in the COVID-19 Era: Virtual Interview Tips for Success.

Applying to Residency in the COVID-19 Era: Virtual Interview Tips for Success.