Diagnostic criteria and core outcome set development for necrotising otitis externa: the COSNOE Delphi consensus study.

OBJECTIVE: Evidence for necrotising otitis externa (NOE) diagnosis and management is limited, and outcome reporting is heterogeneous. International best practice guidelines were used to develop consensus diagnostic criteria and a core outcome set (COS). METHODS: The study was pre-registered on the Core Outcome Measures in Effectiveness Trials (COMET) database. Systematic literature review identified candidate items. Patient-centred items were identified via a qualitative study. Items and their definitions were refined by multidisciplinary stakeholders in a two-round Delphi exercise and subsequent consensus meeting. RESULTS: The final COS incorporates 36 items within 12 themes: Signs and symptoms; Pain; Advanced Disease Indicators; Complications; Survival; Antibiotic regimes and side effects; Patient comorbidities; Non-antibiotic treatments; Patient compliance; Duration and cessation of treatment; Relapse and readmission; Multidisciplinary team management.Consensus diagnostic criteria include 12 items within 6 themes: Signs and symptoms (oedema, otorrhoea, granulation); Pain (otalgia, nocturnal otalgia); Investigations (microbiology [does not have to be positive], histology [malignancy excluded], positive CT and MRI); Persistent symptoms despite local and/or systemic treatment for at least two weeks; At least one risk factor for impaired immune response; Indicators of advanced disease (not obligatory but mut be reported when present at diagnosis). Stakeholders were unanimous that there is no role for secondary, graded, or optional diagnostic items. The consensus meeting identified themes for future research. CONCLUSION: The adoption of consensus-defined diagnostic criteria and COS facilitates standardised research reporting and robust data synthesis. Inclusion of patient and professional perspectives ensures best practice stakeholder engagement.

Clinical and Etiological Exploration of Ventilator-Associated Pneumonia in the Intensive Care Unit of a Developing Country.

Background Ventilator-associated pneumonia (VAP) is a critical concern in the intensive care unit (ICU), with significant implications for patient outcomes. This retrospective cross-sectional study aimed to determine the prevalence of VAP in an ICU of a developing country, identify the predominant etiological factors, assess patient outcomes, and underscore the need for tailored interventions in high-risk patient groups. Methods This retrospective cross-sectional study included 589 ICU patients who underwent ventilator-assisted breathing for over 48 hours. Among them, 151 developed VAP. The diagnosis was made on clinical, laboratory, and radiological findings, and tracheal aspirate cultures. Exclusions included pediatric patients, less than 48 hours of ventilation, and pre-existing lung infections. Patient data encompassed gender, age, comorbidities, outcomes, admission reasons, isolated microorganisms, and clinical findings. Results 151 patients out of the 589 developed VAP. The age of the patients ranged between 31 to 69 years and the mean age was 45.43 ± 8.92 years. Clinical diagnoses upon ICU admission varied, including sepsis, trauma, stroke, and metabolic disorders. Chest X-rays commonly revealed atelectasis (19.2%), consolidation (21.9%), pleural effusion (11.9%), and lobar pneumonia (45.7%). Tracheal aspirate cultures predominantly isolated multidrug-resistant gram-negative rods, with methicillin-resistant gram-positive cocci and fungal pneumonia prevalent in neutropenic sepsis cases. Notably, only 54 (35.8%) of patients survived, with significantly poorer outcomes observed in sepsis, neutropenic sepsis, and stroke cases compared to trauma and post-operative admissions. Conclusion Multidrug-resistant organisms and the spread of nosocomial infections are the predominant causes of VAP in the ICU. This emphasizes the urgent need for multifaceted interventions to prevent and manage VAP effectively. Developing and implementing targeted strategies, considering the unique challenges faced in resource-constrained healthcare settings can aid in decreasing the mortality associated with it.

Radar sensor based machine learning approach for precise vehicle position estimation.

Estimating vehicles' position precisely is essential in Vehicular Adhoc Networks (VANETs) for their safe, autonomous, and reliable operation. The conventional approaches used for vehicles' position estimation, like Global Positioning System (GPS) and Global Navigation Satellite System (GNSS), pose significant data delays and data transmission errors, which render them ineffective in achieving precision in vehicles' position estimation, especially under dynamic environments. Moreover, the existing radar-based approaches proposed for position estimation utilize the static values of range and azimuth, which make them inefficient in highly dynamic environments. In this paper, we propose a radar-based relative vehicle positioning estimation method. In the proposed method, the dynamic range and azimuth of a Frequency Modulated Continuous Wave radar is utilized to precisely estimate a vehicle's position. In the position estimation process, the speed of the vehicle equipped with the radar sensor, called the reference vehicle, is considered such that a change in the vehicle's speed changes the range and azimuth of the radar sensor. For relative position estimation, the distance and relative speed between the reference vehicle and a nearby vehicle are used. To this end, only those vehicles are considered that have a higher possibility of coming in contact with the reference vehicle. The data recorded by the radar sensor is subsequently utilized to calculate the precision and intersection Over Union (IOU) values. You Only Look Once (YOLO) version 4 is utilized to calculate precision and IOU values from the data captured using the radar sensor. The performance is evaluated under various real-time traffic scenarios in a MATLAB-based simulator. Results show that our proposed method achieves 80.0% precision in position estimation and obtains an IOU value up to 87.14%, thereby outperforming the state-of-the-art.

Pathologic Complete Response Achieved in Early-Stage HER2-Positive Breast Cancer After Neoadjuvant Therapy With Trastuzumab and Chemotherapy vs. Trastuzumab, Chemotherapy, and Pertuzumab: A Systematic Review and Meta-Analysis of Clinical Trials.

Patients diagnosed with human epidermal growth factor receptor 2 (HER2)-positive breast cancer require treatment upfront because of the aggressive nature of this type of cancer. Patients with early-stage HER2-positive breast cancer are usually treated with neoadjuvant therapy. This neoadjuvant therapy comprises targeted therapy and chemotherapy. Targeted therapy is given with trastuzumab. Pertuzumab is either administered or not with trastuzumab as a targeted therapy. This systematic review and meta-analysis aim to find out and compare the benefit achieved in terms of pathologic complete response (pCR) by adding pertuzumab to the neoadjuvant treatment regimen for early-stage HER2-positive breast cancer patients. Various databases were searched to find out relevant clinical trials. After going through PubMed, Embase, and Cochrane, three clinical trials were shortlisted for this systematic review and meta-analysis. These three clinical trials were double-armed. Pertuzumab was present in one arm while being absent in one arm to assess the benefit of adding pertuzumab in terms of pCR achieved. Data were analyzed using RevMan Web (Cochrane, London, UK). The odds ratio and 95% confidence interval were calculated for the outcome. The Mantel-Haenszel method and random effect model were used for analysis. The risk of bias in studies was evaluated using the Cochrane risk of bias tool for randomized controlled trials (ROB2). The summary statistics showed that the incidence of pCR was more in the experimental group (having pertuzumab) as compared to the control group (without pertuzumab) with an odds ratio of 2.10 (95% CI: 1.56-2.83) with I2 = 0%. In three double-arm trials, there were 840 participants, 445 in the experimental group and 395 in the control group. A total of 203 (45%) patients out of 445 in the experimental group achieved pCR, whereas 127 (32%) patients out of 395 in the control group achieved pCR. Through the results of this study, it can be concluded that the rate of pCR achieved was higher in that arm in which pertuzumab was present compared to the study arm in which only trastuzumab was given as targeted therapy. Thus, it can be suggested that pertuzumab be added to the neoadjuvant regimen for early-stage HER2-positive breast cancer patients. This would result in achieving a better pCR. And by improving pCR rates, the survival outcomes of patients can be significantly improved.

Assessment of risk factors associated with outbreak of hepatitis A in Shakrial, Rawalpindi, Pakistan.

INTRODUCTION: Two patients with jaundice reported to the National Institute of Health (NIH), Islamabad from Shakrial, Rawalpindi in April 2017. An outbreak investigation team was formulated to assess the disease magnitude, risk factors and control measures. METHODOLOGY: A case-control study was conducted in 360 houses in May 2017. The case definition was: onset of acute jaundice with any symptom including fever, right upper-quadrant pain, loss of appetite, dark urine, nausea and vomiting among Shakrial residents from March 10 - May 19, 2017. Four age and gender matched controls were selected for each case. Blood samples were sent to the NIH for laboratory confirmation. Frequencies, attack rates (AR), odd ratios, and logistic regression were computed at 95% confidence interval and p < 0.05. RESULTS: A total of 25 cases (23 new) were identified with mean age 8 years and male to female ratio 1.5:1. Overall AR was 1.39% and the most severely affected age-group was 5-10 years (AR of 3.92%). Multivariate analysis revealed that raw vegetable consumption, lack of awareness and poor handwashing practices had significant association with disease spread. All blood samples were positive for hepatitis A, and no resident was previously vaccinated. Lack of awareness of disease spread among the community was the most probable reason for the outbreak. There were no new cases during follow up until May 30, 2017. CONCLUSIONS: Healthcare departments should implement public policies towards the management of hepatitis A in Pakistan. Health awareness sessions and vaccination for children ≤ 16 years age is recommended.

Hypokalemic Quadriparesis As Initial Presentation of Secondary Sjogren Syndrome With Associated Autoimmune Thyroiditis: A Case Report.

Sjögren's syndrome is an autoimmune disorder typically presenting as dry mouth and eyes (sicca syndrome). However, the disease can involve any organ, complicating diagnosis. Renal involvement may manifest as distal renal tubular acidosis, leading to hypokalemia. We report a case of a 25-year-old woman presenting with progressive quadriparesis and vomiting. She had severe hypokalemic paralysis due to distal renal tubular acidosis. The patient was diagnosed with secondary Sjögren syndrome with autoimmune thyroiditis. She recovered completely with potassium supplementation.

A secure key dependent dynamic substitution method for symmetric cryptosystems.

The biggest challenge for symmetric cryptosystems is to replace their static substitution with dynamic substitution, because static substitution S-boxes make the symmetric block ciphers more vulnerable to attacks. Previous well-known dynamic key-dependent S-boxes are lacking in dynamicity and do not provide optimal security for symmetric block ciphers. Therefore, this research aims to contribute an effective and secure method for designing key-dependent dynamic S-box with dynamic permutations to make the symmetric block ciphers optimally secure. The proposed S-box method has been experimentally evaluated through several measures such as bit independence criteria, non-linearity, hamming distance, balanced output, strict avalanche criteria including differential and linear approximation probabilities. Moreover, the randomness properties of proposed method have also been evaluated through several standard statistical tests as recommended by the National Institute of Standards and Technology (NIST). Thus, the results show that the proposed method, not only retains effective randomness properties but it also contains, good avalanche effect (up to 62.32%) which is significantly improved than others. Therefore, the proposed substitution method is highly sensitive to the secret key because, only a single bit change in key generates an entirely new S-box with all 256 values at different positions. Thus, the overall evaluation shows that the proposed substitution method is optimally secure and outperforming as compared to the existing S-box techniques. In future, the proposed method can be extended for different key sizes (192-256 bits) or even more.

MIND: A Multi-Source Data Fusion Scheme for Intrusion Detection in Networks.

In recent years, there is an exponential explosion of data generation, collection, and processing in computer networks. With this expansion of data, network attacks have also become a congenital problem in complex networks. The resource utilization, complexity, and false alarm rates are major challenges in current Network Intrusion Detection Systems (NIDS). The data fusion technique is an emerging technology that merges data from multiple sources to form more certain, precise, informative, and accurate data. Moreover, most of the earlier intrusion detection models suffer from overfitting problems and lack optimal detection of intrusions. In this paper, we propose a multi-source data fusion scheme for intrusion detection in networks (MIND) , where data fusion is performed by the horizontal emergence of two datasets. For this purpose, the Hadoop MapReduce tool such as, Hive is used. In addition, a machine learning ensemble classifier is used for the fused dataset with fewer parameters. Finally, the proposed model is evaluated with a 10-fold-cross validation technique. The experiments show that the average accuracy, detection rate, false positive rate, true positive rate, and F-measure are 99.80%, 99.80%, 0.29%, 99.85%, and 99.82% respectively. Moreover, the results indicate that the proposed model is significantly effective in intrusion detection compared to other state-of-the-art methods.

Optimal sequence for chain matrix multiplication using evolutionary algorithm.

The Chain Matrix Multiplication Problem (CMMP) is an optimization problem that helps to find the optimal way of parenthesization for Chain Matrix Multiplication (CMM). This problem arises in various scientific applications such as in electronics, robotics, mathematical programing, and cryptography. For CMMP the researchers have proposed various techniques such as dynamic approach, arithmetic approach, and sequential multiplication. However, these techniques are deficient for providing optimal results for CMMP in terms of computational time and significant amount of scalar multiplication. In this article, we proposed a new model to minimize the Chain Matrix Multiplication (CMM) operations based on group counseling optimizer (GCO). Our experimental results and their analysis show that the proposed GCO model has achieved significant reduction of time with efficient speed when compared with sequential chain matrix multiplication approach. The proposed model provides good performance and reduces the multiplication operations varying from 45% to 96% when compared with sequential multiplication. Moreover, we evaluate our results with the best known dynamic programing and arithmetic multiplication approaches, which clearly demonstrate that proposed model outperforms in terms of computational time and space complexity.

Metagenomic Functional Potential Predicts Degradation Rates of a Model Organophosphorus Xenobiotic in Pesticide Contaminated Soils.

Chemical contamination of natural and agricultural habitats is an increasing global problem and a major threat to sustainability and human health. Organophosphorus (OP) compounds are one major class of contaminant and can undergo microbial degradation, however, no studies have applied system-wide ecogenomic tools to investigate OP degradation or use metagenomics to understand the underlying mechanisms of biodegradation in situ and predict degradation potential. Thus, there is a lack of knowledge regarding the functional genes and genomic potential underpinning degradation and community responses to contamination. Here we address this knowledge gap by performing shotgun sequencing of community DNA from agricultural soils with a history of pesticide usage and profiling shifts in functional genes and microbial taxa abundance. Our results showed two distinct groups of soils defined by differing functional and taxonomic profiles. Degradation assays suggested that these groups corresponded to the organophosphorus degradation potential of soils, with the fastest degrading community being defined by increases in transport and nutrient cycling pathways and enzymes potentially involved in phosphorus metabolism. This was against a backdrop of taxonomic community shifts potentially related to contamination adaptation and reflecting the legacy of exposure. Overall our results highlight the value of using holistic system-wide metagenomic approaches as a tool to predict microbial degradation in the context of the ecology of contaminated habitats.

Extending SEQenv: a taxa-centric approach to environmental annotations of 16S rDNA sequences.

Understanding how the environment selects a given taxon and the diversity patterns that emerge as a result of environmental filtering can dramatically improve our ability to analyse any environment in depth as well as advancing our knowledge on how the response of different taxa can impact each other and ecosystem functions. Most of the work investigating microbial biogeography has been site-specific, and logical environmental factors, rather than geographical location, may be more influential on microbial diversity. SEQenv, a novel pipeline aiming to provide environmental annotations of sequences emerged to provide a consistent description of the environmental niches using the ENVO ontology. While the pipeline provides a list of environmental terms on the basis of sample datasets and, therefore, the annotations obtained are at the dataset level, it lacks a taxa centric approach to environmental annotation. The work here describes an extension developed to enhance the SEQenv pipeline, which provided the means to directly generate environmental annotations for taxa under different contexts. 16S rDNA amplicon datasets belonging to distinct biomes were selected to illustrate the applicability of the extended SEQenv pipeline. A literature survey of the results demonstrates the immense importance of sequence level environmental annotations by illustrating the distribution of both taxa across environments as well as the various environmental sources of a specific taxon. Significantly enhancing the SEQenv pipeline in the process, this information would be valuable to any biologist seeking to understand the various taxa present in the habitat and the environment they originated from, enabling a more thorough analysis of which lineages are abundant in certain habitats and the recovery of patterns in taxon distribution across different habitats and environmental gradients.

Seqenv: linking sequences to environments through text mining.

Understanding the distribution of taxa and associated traits across different environments is one of the central questions in microbial ecology. High-throughput sequencing (HTS) studies are presently generating huge volumes of data to address this biogeographical topic. However, these studies are often focused on specific environment types or processes leading to the production of individual, unconnected datasets. The large amounts of legacy sequence data with associated metadata that exist can be harnessed to better place the genetic information found in these surveys into a wider environmental context. Here we introduce a software program, seqenv, to carry out precisely such a task. It automatically performs similarity searches of short sequences against the "nt" nucleotide database provided by NCBI and, out of every hit, extracts-if it is available-the textual metadata field. After collecting all the isolation sources from all the search results, we run a text mining algorithm to identify and parse words that are associated with the Environmental Ontology (EnvO) controlled vocabulary. This, in turn, enables us to determine both in which environments individual sequences or taxa have previously been observed and, by weighted summation of those results, to summarize complete samples. We present two demonstrative applications of seqenv to a survey of ammonia oxidizing archaea as well as to a plankton paleome dataset from the Black Sea. These demonstrate the ability of the tool to reveal novel patterns in HTS and its utility in the fields of environmental source tracking, paleontology, and studies of microbial biogeography. To install seqenv, go to: https://github.com/xapple/seqenv.

PAIgG and PAIgM levels in secondary dengue virus infections lead to thrombocytopenia in patients from KP, Pakistan

Objective: To understand the impact of platelet associated immunoglobulin G (PAIgG)/platelet associated immunoglobulin M (PAIgM) on severity of dengue virus infection leading to thrombocytopenia. Methods: In this study we examined a total of 52 patients who were having secondary infection of dengue in acute phase by using competitive ELISA. Results: A decrease in the platelet count was observed at the acute phase of infection while all along the recovery stage the count of platelet was significantly increased. A significant decrease was observed in PAIgG and PAIgM in these subjects. Inverse correlation was found between platelets count and PAIgG/PAIgM among the subjects studied. In the platelets elution from ten subjects, anti-dengue virus immunoglobulin G and immunoglobulin M were observed. PAIgG and PAIgM with inclined levels were higher in dengue hemorrhagic fever than the classical dengue fever. In the development of dengue hemorrhagic fever PAIgM inclined level was independently associated with high specificity, showing a possible indication of dengue hemorrhagic fever. Conclusions: This study suggests that in secondary dengue virus infection, the PAIgG and PAIgM levels, and the activity of anti-dengue virus play key roles, both in the development and severity of the disease.

MKEM: a Multi-level Knowledge Emergence Model for mining undiscovered public knowledge.

BACKGROUND: Since Swanson proposed the Undiscovered Public Knowledge (UPK) model, there have been many approaches to uncover UPK by mining the biomedical literature. These earlier works, however, required substantial manual intervention to reduce the number of possible connections and are mainly applied to disease-effect relation. With the advancement in biomedical science, it has become imperative to extract and combine information from multiple disjoint researches, studies and articles to infer new hypotheses and expand knowledge. METHODS: We propose MKEM, a Multi-level Knowledge Emergence Model, to discover implicit relationships using Natural Language Processing techniques such as Link Grammar and Ontologies such as Unified Medical Language System (UMLS) MetaMap. The contribution of MKEM is as follows: First, we propose a flexible knowledge emergence model to extract implicit relationships across different levels such as molecular level for gene and protein and Phenomic level for disease and treatment. Second, we employ MetaMap for tagging biological concepts. Third, we provide an empirical and systematic approach to discover novel relationships. RESULTS: We applied our system on 5000 abstracts downloaded from PubMed database. We performed the performance evaluation as a gold standard is not yet available. Our system performed with a good precision and recall and we generated 24 hypotheses. CONCLUSIONS: Our experiments show that MKEM is a powerful tool to discover hidden relationships residing in extracted entities that were represented by our Substance-Effect-Process-Disease-Body Part (SEPDB) model.