Health-Seeking Behaviour and Its Determinants Following the COVID-19 Pandemic.

OBJECTIVE: The purpose of this study was to determine the change in behaviour of individuals towards any health issues they faced after the coronavirus disease 2019 (COVID-19) pandemic and to compare the health-seeking behaviour of people who were infected by the virus and those who were not infected. METHODS: A cross-sectional study was conducted among 400 participants visiting Shifa International Hospital, Islamabad, Pakistan, and Pakistan Institute of Medical Sciences Hospital, Islamabad, Pakistan. Data was collected through a pilot-tested questionnaire and analyzed using IBM SPSS Statistics for Windows, Version 26.0 (Released 2019; IBM Corp., Armonk, New York, United States). RESULTS: In 286 participants (71.6%), health-seeking behaviours were significantly altered by the COVID-19 pandemic. Overall, this research showed that COVID-19 was linked to poor health-seeking behaviour. CONCLUSION: Most of the participants' health-seeking behaviours were significantly altered by the COVID-19 pandemic. A significant change in how people behaved towards any health problem was reported. As a result, public awareness campaigns should focus on delivering more information about COVID-19 to promote their health-seeking behaviour.

Bacterial ventriculoperitoneal shunt infections: changing trends in antimicrobial susceptibility, a 7-year retrospective study from Pakistan.

BACKGROUND: Ventriculoperitoneal (VP) shunt infections in adults represent a severe complication and make treatment more challenging. Therefore, drug susceptibility patterns are crucial for therapeutic decisions and infection control in neurosurgical centers. This 7-year retrospective study aimed to identify the bacteria responsible for adult VP shunt infections and determine their drug susceptibility patterns. METHODS: This single-center study was performed from 2015 to 2021 in Lahore, Pakistan, and included CSF cultures from VP shunt infections. Demographic data, causative organisms, and antimicrobial susceptibility testing results were collected. Multivariate analysis of variance (MANOVA) and two-sample t-tests were used to analyze and compare the antibiotic sensitivity trends over the study period. RESULTS: 14,473 isolates recovered from 13,937 CSF samples of VP shunt infections were identified and analyzed for their susceptibility patterns to antimicrobials. The proportion of Gram-negative and Gram-positive bacteria were 11,030 (76%) and 3443 (24)%, respectively. The predominant bacteria were Acinetobacter species (n = 5898, 41%), followed by Pseudomonas species (n = 2368, 16%) and coagulase-negative Staphylococcus (CoNS) (n = 1880, 13%). 100% of Staphylococcus aureus (S.aureus) and CoNS were sensitive to vancomycin and linezolid (n = 2580). However, 52% of S. aureus (719/1,343) were methicillin-resistant Staphylococcus aureus (MRSA). Acinetobacter showed maximum sensitivity to meropenem at 69% (2759/4768). Pseudomonas was 80% (1385/1863 sensitive to piperacillin-tazobactam, Escherichia coli (E. coli) showed 72% to amikacin (748/1055), while Klebsiella spp. was 57% (574/1170) sensitive to piperacillin-tazobactam. The sensitivity of piperacillin-tazobactam and meropenem for Gram-negative bacteria decreased significantly (p < 0.05) over 7 years, with 92.2% and 88.91% sensitive in 2015 and 66.7% and 62.8% sensitive in 2021, respectively. CONCLUSION: The significant decrease in the effectiveness of carbapenem and beta-lactam/beta-lactamase inhibitor combination drugs for the common Gram-negative causative agents of VP shunt infections suggests that alternative antibiotics such as colistin, fosfomycin, ceftazidime/avibactam, ceftolozane/tazobactam, and tigecycline should be considered and in consequence included in testing panels. Additionally, it is recommended to adopt care bundles for the prevention of VP shunt infection.

Unleashing the potential of AI for pathology: challenges and recommendations.

Computational pathology is currently witnessing a surge in the development of AI techniques, offering promise for achieving breakthroughs and significantly impacting the practices of pathology and oncology. These AI methods bring with them the potential to revolutionize diagnostic pipelines as well as treatment planning and overall patient care. Numerous peer-reviewed studies reporting remarkable performance across diverse tasks serve as a testimony to the potential of AI in the field. However, widespread adoption of these methods in clinical and pre-clinical settings still remains a challenge. In this review article, we present a detailed analysis of the major obstacles encountered during the development of effective models and their deployment in practice. We aim to provide readers with an overview of the latest developments, assist them with insights into identifying some specific challenges that may require resolution, and suggest recommendations and potential future research directions. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

An aggregation of aggregation methods in computational pathology.

Image analysis and machine learning algorithms operating on multi-gigapixel whole-slide images (WSIs) often process a large number of tiles (sub-images) and require aggregating predictions from the tiles in order to predict WSI-level labels. In this paper, we present a review of existing literature on various types of aggregation methods with a view to help guide future research in the area of computational pathology (CPath). We propose a general CPath workflow with three pathways that consider multiple levels and types of data and the nature of computation to analyse WSIs for predictive modelling. We categorize aggregation methods according to the context and representation of the data, features of computational modules and CPath use cases. We compare and contrast different methods based on the principle of multiple instance learning, perhaps the most commonly used aggregation method, covering a wide range of CPath literature. To provide a fair comparison, we consider a specific WSI-level prediction task and compare various aggregation methods for that task. Finally, we conclude with a list of objectives and desirable attributes of aggregation methods in general, pros and cons of the various approaches, some recommendations and possible future directions.

REET: robustness evaluation and enhancement toolbox for computational pathology.

MOTIVATION: Digitization of pathology laboratories through digital slide scanners and advances in deep learning approaches for objective histological assessment have resulted in rapid progress in the field of computational pathology (CPath) with wide-ranging applications in medical and pharmaceutical research as well as clinical workflows. However, the estimation of robustness of CPath models to variations in input images is an open problem with a significant impact on the downstream practical applicability, deployment and acceptability of these approaches. Furthermore, development of domain-specific strategies for enhancement of robustness of such models is of prime importance as well. RESULTS: In this work, we propose the first domain-specific Robustness Evaluation and Enhancement Toolbox (REET) for computational pathology applications. It provides a suite of algorithmic strategies for enabling robustness assessment of predictive models with respect to specialized image transformations such as staining, compression, focusing, blurring, changes in spatial resolution, brightness variations, geometric changes as well as pixel-level adversarial perturbations. Furthermore, REET also enables efficient and robust training of deep learning pipelines in computational pathology. Python implementation of REET is available at https://github.com/alexjfoote/reetoolbox. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MILAMP: Multiple Instance Prediction of Amyloid Proteins.

Amyloid proteins are implicated in several diseases such as Parkinson's, Alzheimer's, prion diseases, etc. In order to characterize the amyloidogenicity of a given protein, it is important to locate the amyloid forming hotspot regions within the protein as well as to analyze the effects of mutations on these proteins. The biochemical and biological assays used for this purpose can be facilitated by computational means. This paper presents a machine learning method that can predict hotspot amyloidogenic regions within proteins and characterize changes in their amyloidogenicity due to point mutations. The proposed method called MILAMP (Multiple Instance Learning of AMyloid Proteins) achieves high accuracy for identification of amyloid proteins, hotspot localization, and prediction of mutation effects on amyloidogenicity by integrating heterogenous data sources and exploiting common predictive patterns across these tasks through multiple instance learning. The paper presents comprehensive benchmarking experiments to test the predictive performance of MILAMP in comparison to previously published state of the art techniques for amyloid prediction. The python code for the implementation and webserver for MILAMP is available at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#MILAMP.

AcrDB: a database of anti-CRISPR operons in prokaryotes and viruses.

CRISPR-Cas is an anti-viral mechanism of prokaryotes that has been widely adopted for genome editing. To make CRISPR-Cas genome editing more controllable and safer to use, anti-CRISPR proteins have been recently exploited to prevent excessive/prolonged Cas nuclease cleavage. Anti-CRISPR (Acr) proteins are encoded by (pro)phages/(pro)viruses, and have the ability to inhibit their host's CRISPR-Cas systems. We have built an online database AcrDB (http://bcb.unl.edu/AcrDB) by scanning ∼19 000 genomes of prokaryotes and viruses with AcrFinder, a recently developed Acr-Aca (Acr-associated regulator) operon prediction program. Proteins in Acr-Aca operons were further processed by two machine learning-based programs (AcRanker and PaCRISPR) to obtain numerical scores/ranks. Compared to other anti-CRISPR databases, AcrDB has the following unique features: (i) It is a genome-scale database with the largest collection of data (39 799 Acr-Aca operons containing Aca or Acr homologs); (ii) It offers a user-friendly web interface with various functions for browsing, graphically viewing, searching, and batch downloading Acr-Aca operons; (iii) It focuses on the genomic context of Acr and Aca candidates instead of individual Acr protein family and (iv) It collects data with three independent programs each having a unique data mining algorithm for cross validation. AcrDB will be a valuable resource to the anti-CRISPR research community.

Machine learning predicts new anti-CRISPR proteins.

The increasing use of CRISPR-Cas9 in medicine, agriculture, and synthetic biology has accelerated the drive to discover new CRISPR-Cas inhibitors as potential mechanisms of control for gene editing applications. Many anti-CRISPRs have been found that inhibit the CRISPR-Cas adaptive immune system. However, comparing all currently known anti-CRISPRs does not reveal a shared set of properties for facile bioinformatic identification of new anti-CRISPR families. Here, we describe AcRanker, a machine learning based method to aid direct identification of new potential anti-CRISPRs using only protein sequence information. Using a training set of known anti-CRISPRs, we built a model based on XGBoost ranking. We then applied AcRanker to predict candidate anti-CRISPRs from predicted prophage regions within self-targeting bacterial genomes and discovered two previously unknown anti-CRISPRs: AcrllA20 (ML1) and AcrIIA21 (ML8). We show that AcrIIA20 strongly inhibits Streptococcus iniae Cas9 (SinCas9) and weakly inhibits Streptococcus pyogenes Cas9 (SpyCas9). We also show that AcrIIA21 inhibits SpyCas9, Streptococcus aureus Cas9 (SauCas9) and SinCas9 with low potency. The addition of AcRanker to the anti-CRISPR discovery toolkit allows researchers to directly rank potential anti-CRISPR candidate genes for increased speed in testing and validation of new anti-CRISPRs. A web server implementation for AcRanker is available online at http://acranker.pythonanywhere.com/.

Granulocyte Colony Stimulating Factor in Burn Patients with Neutropenia.

OBJECTIVE: To assess the role of granulocyte-colony stimulating factor (G-CSF) for improving neutropenia in burns patients with neutropenia. STUDY DESIGN: Experimental study. PLACE AND DURATION OF STUDY: Jinnah Burn and Reconstructive Surgery Centre, Lahore, from May to October 2017. METHODOLOGY: Patients with burn injury, having absolute neutrophil count (ANC) <500 / µL or where it was expected to decrease to <500/µL within the next 48 hours, were recruited in the study. A detailed demographic profile of patients was taken, burn site was evaluated, and sample collection by phlebotomy was done in the complete blood count (CBC) vial. Samples were run in a CBC analyser and verification of neutrophil count on the neubuar chamber was done. ANC was taken for 3 days for each patient. Injection Filgrastim was given 300 µg subcutaneous (S/C) or intravenous (I/V) once daily until the neutropenia improved. Improvement was categorised as good, moderate and poor, depending on the number of days for improvement in ANC. The response was further stratified on the basis of age, gender and percentage of burn. RESULTS: A total of 39 patients with mean age of 32.1±14.4 years included 84.6% (n=33) males and 15.4% (n=6) females. Mean percentage of burn was 40.5±15.7%. In 12-40 years of age, there were 30/39 (76.9%) patients. Among them, 11/30 (36.6%) were good, 13/30 (43.3%) were moderate, and 6/30 (20%) were poor responders. In 41-70 years of age, there were 9/39 (23.1%) patients. Among them, 2/9 (22.2%) were good, 4/9 (44.44%) were moderate, and 3/9 (33.3%) were poor responders (p = 0.616). CONCLUSION: The addition of G-CSF injections to the standard treatment of burn injury markedly improve the neutrophil counts in burn patients with neutropenia.

Learning protein binding affinity using privileged information.

BACKGROUND: Determining protein-protein interactions and their binding affinity are important in understanding cellular biological processes, discovery and design of novel therapeutics, protein engineering, and mutagenesis studies. Due to the time and effort required in wet lab experiments, computational prediction of binding affinity from sequence or structure is an important area of research. Structure-based methods, though more accurate than sequence-based techniques, are limited in their applicability due to limited availability of protein structure data. RESULTS: In this study, we propose a novel machine learning method for predicting binding affinity that uses protein 3D structure as privileged information at training time while expecting only protein sequence information during testing. Using the method, which is based on the framework of learning using privileged information (LUPI), we have achieved improved performance over corresponding sequence-based binding affinity prediction methods that do not have access to privileged information during training. Our experiments show that with the proposed framework which uses structure only during training, it is possible to achieve classification performance comparable to that which is obtained using structure-based features. Evaluation on an independent test set shows improved performance over the PPA-Pred2 method as well. CONCLUSIONS: The proposed method outperforms several baseline learners and a state-of-the-art binding affinity predictor not only in cross-validation, but also on an additional validation dataset, demonstrating the utility of the LUPI framework for problems that would benefit from classification using structure-based features. The implementation of LUPI developed for this work is expected to be useful in other areas of bioinformatics as well.

Training host-pathogen protein-protein interaction predictors.

Detection of protein-protein interactions (PPIs) plays a vital role in molecular biology. Particularly, pathogenic infections are caused by interactions of host and pathogen proteins. It is important to identify host-pathogen interactions (HPIs) to discover new drugs to counter infectious diseases. Conventional wet lab PPI detection techniques have limitations in terms of cost and large-scale application. Hence, computational approaches are developed to predict PPIs. This study aims to develop machine learning models to predict inter-species PPIs with a special interest in HPIs. Specifically, we focus on seeking answers to three questions that arise while developing an HPI predictor: (1) How should negative training examples be selected? (2) Does assigning sample weights to individual negative examples based on their similarity to positive examples improve generalization performance? and, (3) What should be the size of negative samples as compared to the positive samples during training and evaluation? We compare two available methods for negative sampling: random versus DeNovo sampling and our experiments show that DeNovo sampling offers better accuracy. However, our experiments also show that generalization performance can be improved further by using a soft DeNovo approach that assigns sample weights to negative examples inversely proportional to their similarity to known positive examples during training. Based on our findings, we have also developed an HPI predictor called HOPITOR (Host-Pathogen Interaction Predictor) that can predict interactions between human and viral proteins. The HOPITOR web server can be accessed at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#HoPItor .

Correlation Filters for Detection of Cellular Nuclei in Histopathology Images.

Nuclei detection in histology images is an essential part of computer aided diagnosis of cancers and tumors. It is a challenging task due to diverse and complicated structures of cells. In this work, we present an automated technique for detection of cellular nuclei in hematoxylin and eosin stained histopathology images. Our proposed approach is based on kernelized correlation filters. Correlation filters have been widely used in object detection and tracking applications but their strength has not been explored in the medical imaging domain up till now. Our experimental results show that the proposed scheme gives state of the art accuracy and can learn complex nuclear morphologies. Like deep learning approaches, the proposed filters do not require engineering of image features as they can operate directly on histopathology images without significant preprocessing. However, unlike deep learning methods, the large-margin correlation filters developed in this work are interpretable, computationally efficient and do not require specialized or expensive computing hardware. AVAILABILITY: A cloud based webserver of the proposed method and its python implementation can be accessed at the following URL: http://faculty.pieas.edu.pk/fayyaz/software.html#corehist .

CaMELS: In silico prediction of calmodulin binding proteins and their binding sites.

Due to Ca2+ -dependent binding and the sequence diversity of Calmodulin (CaM) binding proteins, identifying CaM interactions and binding sites in the wet-lab is tedious and costly. Therefore, computational methods for this purpose are crucial to the design of such wet-lab experiments. We present an algorithm suite called CaMELS (CalModulin intEraction Learning System) for predicting proteins that interact with CaM as well as their binding sites using sequence information alone. CaMELS offers state of the art accuracy for both CaM interaction and binding site prediction and can aid biologists in studying CaM binding proteins. For CaM interaction prediction, CaMELS uses protein sequence features coupled with a large-margin classifier. CaMELS models the binding site prediction problem using multiple instance machine learning with a custom optimization algorithm which allows more effective learning over imprecisely annotated CaM-binding sites during training. CaMELS has been extensively benchmarked using a variety of data sets, mutagenic studies, proteome-wide Gene Ontology enrichment analyses and protein structures. Our experiments indicate that CaMELS outperforms simple motif-based search and other existing methods for interaction and binding site prediction. We have also found that the whole sequence of a protein, rather than just its binding site, is important for predicting its interaction with CaM. Using the machine learning model in CaMELS, we have identified important features of protein sequences for CaM interaction prediction as well as characteristic amino acid sub-sequences and their relative position for identifying CaM binding sites. Python code for training and evaluating CaMELS together with a webserver implementation is available at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#camels.

CAFÉ-Map: Context Aware Feature Mapping for mining high dimensional biomedical data.

Feature selection and ranking is of great importance in the analysis of biomedical data. In addition to reducing the number of features used in classification or other machine learning tasks, it allows us to extract meaningful biological and medical information from a machine learning model. Most existing approaches in this domain do not directly model the fact that the relative importance of features can be different in different regions of the feature space. In this work, we present a context aware feature ranking algorithm called CAFÉ-Map. CAFÉ-Map is a locally linear feature ranking framework that allows recognition of important features in any given region of the feature space or for any individual example. This allows for simultaneous classification and feature ranking in an interpretable manner. We have benchmarked CAFÉ-Map on a number of toy and real world biomedical data sets. Our comparative study with a number of published methods shows that CAFÉ-Map achieves better accuracies on these data sets. The top ranking features obtained through CAFÉ-Map in a gene profiling study correlate very well with the importance of different genes reported in the literature. Furthermore, CAFÉ-Map provides a more in-depth analysis of feature ranking at the level of individual examples. AVAILABILITY: CAFÉ-Map Python code is available at: http://faculty.pieas.edu.pk/fayyaz/software.html#cafemap . The CAFÉ-Map package supports parallelization and sparse data and provides example scripts for classification. This code can be used to reconstruct the results given in this paper.