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Classification is a data mining task, which plays a vital role in clinical diagnosis. Irrelevant features will reduce the classifier accuracy. Only a subset of features in a clinical dataset plays a key role in diagnosing the disease. Thus, selecting the relevant features and training the classifier will improve the classifier accuracy. Clinical datasets are subjected to pre-processing, followed by feature selection and classification. In this work, a framework that uses Synergistic firefly algorithm for feature selection and an ensemble classifier for classification has been designed and implemented. The missing values in the clinical datasets are handled using the k-Nearest Neighbour (k-NN) technique. Min–max normalization is used to normalize the data and the normalized data are split into training and testing sets using tenfold cross-validation. Feature selection has been carried out using a wrapper approach that uses the Synergistic firefly algorithm for generating feature subsets and the Levenberg-Marquardt Back Propagation Neural Network to evaluate the performance of the subsets. The ensemble classifier, consisting of the Levenberg-Marquardt Back Propagation Neural Network, Extreme Learning Machine and Naïve Bayes classifier, is trained using the optimal features selected by the Synergistic firefly algorithm. Experimentation has been carried out using eight clinical datasets from the University of California Irvine (UCI) machine learning repository and it has been inferred that selecting the relevant features improves the classifier accuracy. The results obtained prove the efficiency of the decision-making system developed using Synergistic Firefly and the decision-making system can assist the clinician in decision-making to diagnose diseases.

A framework for Computer-Aided Diagnosis (CAD) to diagnose Cavitary TB and Miliary TB from chest Computed Tomography (CT) slices has been designed and implemented. The lung tissues from the CT slices are segmented using region-based Active Contour Model (ACM) and the Region of Interests (ROIs) labelled by an expert radiologist are extracted. Features based on shape and texture are extracted from each ROI. A wrapper-based Improved Artificial Bee Colony Optimization (I-ABCO) algorithm with the accuracy of the Support Vector Machine (SVM) classifier as the fitness function is used to select the optimal subset of features. The search process of I-ABCO is improved using two evaluation functions, namely, rough dependency measure (RDM) and mutual information (MI), to promote better exploitation of the search space. The selected features are used to train the Radial Basis Function Neural Network (RBFNN) classifier, using ten-fold cross validation and the performance is evaluated. Experimentation has been performed on CT slices using two datasets: Tuberculosis dataset and Lung Image Database Consortium-Image Database Resource Initiative (LIDC-IDRI) dataset. The accuracy of the CAD systems using I-ABCO with MI and I-ABCO with RDM for both datasets are (88.34%, 92.63%) and (87.32%, 90.17%), respectively. The CAD system using the basic Artificial Bee Colony Optimization (ABCO) algorithm for feature selection is also experimented with the same datasets and an accuracy of (85.68%, 88%) is achieved. The performance of the proposed algorithms outperforms the ABCO algorithm in diagnostic accuracy, which ensures that selecting the optimal feature subset efficiently has an impact on the classification accuracy.

Embryo-derived tissue-resident macrophages are the first representatives of the haematopoietic lineage to emerge in metazoans. In mammals, resident macrophages originate from early yolk sac progenitors and are specified into tissue-specific subsets during organogenesis—establishing stable spatial and functional relationships with specialized tissue cells—and persist in adults. Resident macrophages are an integral part of tissues together with specialized cells: for instance, microglia reside with neurons in brain, osteoclasts reside with osteoblasts in bone, and fat-associated macrophages reside with white adipocytes in adipose tissue. This ancillary cell type, which is developmentally and functionally distinct from haematopoietic stem cell and monocyte-derived macrophages, senses and integrates local and systemic information to provide specialized tissue cells with the growth factors, nutrient recycling and waste removal that are critical for tissue growth, homeostasis and repair. Resident macrophages contribute to organogenesis, promote tissue regeneration following damage and contribute to tissue metabolism and defence against infectious disease. A correlate is that genetic or environment-driven resident macrophage dysfunction is a cause of degenerative, metabolic and possibly inflammatory and tumoural diseases. In this Review, we aim to provide a conceptual outline of our current understanding of macrophage physiology and its importance in human diseases, which may inform and serve the design of future studies.

A 31-year-old female presented to the emergency department with abdominal pain, vomiting and constipation. Serum sodium levels were recorded at 110 mmol/L on admission, dropping to 96 mmol/L despite fluid restriction. The patient developed hallucinations and required hypertonic saline administration in critical care. Urinary sodium was detected at 149 mmol/L, consistent with syndrome of inappropriate antidiuretic hormone secretion (SiADH). Urinary porphyrins were also raised, consistent with a diagnosis of acute intermittent porphyria with SiADH as a complication.

ABSTRACTCo-operative co-evolution approach solves problems by breaking them into subproblems. The proposed framework for Clinical Decision Support System (CDSS) uses a co-operative coevolution approach which treats Feature Selection (FS) and Instance Selection (IS) as independent subproblems. FS and IS remove less relevant features and instances, respectively, thereby improving the overall performance of the system. In this work, both Feature and Instance selection are done using the wrapper approach, which uses co-operative co-evolution and random forest classifier. The reduced dataset is used to train a random forest classifier and this trained model helped in making clinical decisions. These decisions assist physicians as the second opinion for diagnosis and treatment. Wisconsin Diagnostic Breast Cancer (WDBC), Hepatitis, Pima Indian Diabetes (PID), Cleveland Heart Disease (CHD), Statlog Heart Disease (SHD), Vertebral Column, and Hepatocellular Carcinoma (HCC) datasets from the University of California Irvine (UCI) Machine Learning repository are used for experimentation. The proposed framework achieved an accuracy of 97.1%, 82.3%, 81.01%, 93.4%, 96.8%, 91.4%, and 72.2% for datasets WDBC, Hepatitis, PID, CHD, SHD, vertebral column, and HCC, respectively. The results prove that the CDSS-developed using co-operative coevolution can efficiently assist the physicians in decision-making.

The objective of this multicenter observational study was to evaluate resuscitation volumes and outcomes of patients who underwent fluid resuscitation utilizing the Burn Navigator (BN), a resuscitation clinical decision support tool. Two analyses were performed: examination of the first 24 hours of resuscitation and the first 24 hours postburn regardless of when the resuscitation began, to account for patients who presented in a delayed fashion. Patients were classified as having followed the BN (FBN) if all hourly fluid rates were within ±20 ml of BN recommendations for that hour at least 83% of the time; otherwise, they were classified as not having followed BN (NFBN). Analysis of resuscitation volumes for FBN patients in the first 24 hours resulted in average volumes for primary crystalloid and total fluids administered of 4.07 ± 1.76 ml/kg/TBSA (151.48 ± 77.46 ml/kg) and 4.68 ± 2.06 ml/kg/TBSA (175.01 ± 92.22 ml/kg), respectively. Patients who presented in a delayed fashion revealed average volumes for primary and total fluids of 5.28 ± 2.54 ml/kg/TBSA (201.11 ± 106.53 ml/kg) and 6.35 ± 2.95 ml/kg/TBSA (244.08 ± 133.5 ml/kg), respectively. There was a significant decrease in the incidence of burn shock in the FBN group (P< .05). This study shows that the BN provides comparable resuscitation volumes of primary crystalloid fluid to the Parkland formula, recommends total fluid infusion less than the Ivy index, and was associated with a decreased incidence of burn shock. Early initiation of the BN device resulted in lower overall fluid volumes.

Lamproglena monodiCapart, 1944 adult females were first described from the Democratic Republic of the Congo and have been redescribed seven times from different cichlid species in Egypt, Burkina Faso and Brazil, all showing morphological variation. The adult male was first described from Lake Victoria and the Victoria Nile in Uganda. Copepods from the present study were collected from the gills of Oreochromis niloticus(Linnaeus, 1758) from Kibos Fish Farm, Kenya (between December 2017 and April 2018) and Sharqia Governorate (June 2019) and El-Minia (between July and December 2018) in Egypt. They were identified as L. monodibased on morphology and genetic data. Parasites were studied morphologically with the aid of light microscopy and scanning electron microscopy (SEM), and female infection levels calculated. Genetically, the specimens were studied using fragments of 18S and 28S rDNA. SEM analyses revealed additional diagnostic morphological features for the three adult males (elusive) and 249 females. These data were used to update the identification key for male Lamproglenaspecies. Prevalence, mean intensity and mean abundance of the females were calculated as 13.5%, 8.6 and 1.2 respectively. Both 18S and 28S rDNA fragments confirmed the distinctness of L. monodifrom previously characterised Lamproglenaspecies, with distances of 1.22–1.94% (17–27 bp) for 18S rDNA and 15.66–18.1% (111–128 bp) for 28S rDNA. Haplotypes obtained from male and female specimens were identical, confirming the identity of the male specimens. This is the first genetic study of Lamproglenafrom Africa, the first SEM and genetic study of male L. monodi, and the first geographical report of this parasite from Kenya. The male and molecular data reported herein provide a useful point of reference for future Lamproglenastudies.

The success of deep learning over the traditional machine learning techniques in handling artificial intelligence application tasks such as image processing, computer vision, object detection, speech recognition, medical imaging and so on, has made deep learning the buzz word that dominates Artificial Intelligence applications. From the last decade, the applications of deep learning in physiological signals such as electrocardiogram (ECG) have attracted a good number of research. However, previous surveys have not been able to provide a systematic comprehensive review including biometric ECG based systems of the applications of deep learning in ECG with respect to domain of applications. To address this gap, we conducted a systematic literature review on the applications of deep learning in ECG including biometric ECG based systems. The study analyzed systematically, 150 primary studies with evidence of the application of deep learning in ECG. The study shows that the applications of deep learning in ECG have been applied in different domains. We presented a new taxonomy of the domains of application of the deep learning in ECG. The paper also presented discussions on biometric ECG based systems and meta-data analysis of the studies based on the domain, area, task, deep learning models, dataset sources and preprocessing methods. Challenges and potential research opportunities were highlighted to enable novel research. We believe that this study will be useful to both new researchers and expert researchers who are seeking to add knowledge to the already existing body of knowledge in ECG signal processing using deep learning algorithm.

Non-performing loans is a problem facing many deposit taking SACCOS in Kenya and this if problem is not mitigated will root collapsing of the financial institutions which are of great benefit in financial deepening of a country. Liquidity, as characteristic related to deposit taking SACCOS was analysed to determine at what extent it influences level of NPLs. The period of the study was six years, from 2012 to 2017. The data used was derived from financial statements which are submitted by DT-SACCOs to SASRA offices whereby a total of 119 deposits taking SACCOs were used as the target population of the study. The study recommended level of liquidity level should be adhered to and this will help to control level of non-performing loans.

A new classification framework for a Clinical Decision Support System, utilizing a Genetic algorithm and an Artificial Flora Optimized Neural Network is presented in this paper. GAFON is an artificial neural network whose topology is optimized with Genetic Algorithm and the learnable parameters are optimized with Artificial Flora Optimization algorithm. Drop out technique is used in the topology optimization phase and weight regularization is used in the parameter optimization phase. The proposed method minimizes the co-adaptation problem, reduces over-fitting of training data and improves the generalization of a feed forward neural network. The classification framework developed has been tested for classifying both multi class and binary class clinical datasets. The proposed method attained accuracy values of 86.82% for Hepatitis C Virus (HCV) for Egyptian patients, 84.91% for Vertebral Column 95.65% for Statlog Heart Disease (SHD), SHD and 93.79% for Early Stage Diabetes Risk Prediction (ESDRP), all datasets obtained from UCI repository

The purpose of this study is the examine the effect of a holistic review process on the recruitment of women and students underrepresented in medicine (UIM) in a general surgery residency program.

Availability of low-cost, reliable, and portable Electrocardiography (ECG) devices is still very important in the medical world today. Despite the tremendous technological advancement, Cardiovascular Diseases (CVDs) remain a serious health burden claiming millions of lives on an annual basis globally. This is more prevalent in Low and Middle-Income Countries (LMICs) where there are huge financial instability and lack of critical infrastructure and support services for the health care system. Efforts aimed at reducing the prevalence of CVDs are confounded by late diagnosis, frequently, caused by lack of access to or non-availability of basic diagnostic modalities such as the ECG. Hence effective mitigation of the effect of CVDs in LMICs depend on the development of such devices at low-cost with reliability, accuracy and energy efficiency. This paper therefore, was developed to understand the state of the art of low-cost ECG acquisition systems with respect to design features and system capabilities for different use cases. In addition, different design options and taxonomies of available low-cost ECG devices, case studies reports of efficacy tests have been provided. The paper proposes a generalised ECG framework and provides implementation challenges and open research directions that should be considered when developing such devices for proper management of CVDs.

To provide a framework for effective mentorship of residents underrepresented in medicine (UIM) situated in the context of their experiences in healthcare teams.

AbstractAimTo estimate the incremental phase-specific and lifetime economic burden among newly diagnosed cervical and endometrial cancer patients vs.non-cancer controls.MethodsCervical and endometrial cancer patients newly diagnosed between January 2015 and June 2018 were identified in the Optum Clinformatics DataMart database. The index date was the date of the first diagnosis for cancer cases and the first claim date after 12 months of continuous enrollment for non-cancer controls. Patients were followed until death/loss of enrollment/end of data availability. Per patient per month (PPPM) costs attributable to cancer were calculated for four phases: pre-diagnosis (3 months before diagnosis), initial (6 months post-diagnosis), terminal (6 months pre-death), and continuation (remaining time between initial and terminal phases). Survival data were obtained to determine the monthly proportion of patients in each phase. Total survival adjusted monthly costs were obtained by multiplying the proportion of patients in each phase by the total cost incurred during that month. Phase-specific and lifetime incremental costs of cervical and endometrial cancer were obtained using generalized linear models.ResultsThe analytic cohort included 1,002 cervical cancer patients and 4,005 matched non-cancer controls and 5,003 endometrial cancer patients matched with 19,999 non-cancer controls. Mean adjusted incremental PPPM lifetime costs (95% CI) for cervical cancer and endometrial cancer cases were $5,910 ($5,373–$6,446) and $3,475 ($3,259–$3,691), respectively. Incremental total PPPM phase-specific costs attributable to cervical and endometrial cancer were pre-diagnosis (cervical: $1,057; endometrial: $3,315), initial ($12,084; $8,618), continuation ($2,732; $1,147), and terminal ($2,702; $5,442). Incremental costs were significantly higher for cancer patients vs.non-cancer controls across patient lifetime and all phases of care (except terminal phase costs for cervical cancer). Outpatient costs were the major driver of costs across all post-diagnosis phases.ConclusionThis study highlights the cost burden associated with cervical/endometrial cancer and cost variation by phases of care.

ABSTRACTClinical time series data contain large set of time-stamped data points that describe the patient’s health. The observations of these data points are done at irregular intervals and hence knowledge mining turns challenging. To overcome this, there is a need to reduce the dimension (length) of time series data into smaller representations with minimal loss of information. The objective of this work is to present a forecast-error approximation-based bottom-up (FeAB) segmentation for segmenting and classifying clinical time series data using time delay neural network (TDNN). The proposed approach includes two functionalities namely temporal data summarisation and classification. In temporal data summarisation, clinical time-series data are divided into sequence of temporal interpreted segments using FeAB segmentation. FeAB adopts a double exponential smoothing technique to derive the growth rate, mean and forecast-error for each clinical observation. The obtained forecast-error is used to compute the merge-cost for FeAB segmentation. TDNN classifier builds classification model for the segmented time series. The classifier is trained using backpropagation with Levenberg-Marquardt algorithm. The time series dataset of hepatitis and thrombosis patients are used for experimentation. The results illustrate that the proposed framework has effectively handled the temporal data irregularities and has shown improvement in classification accuracy.

Artificial neural networks (ANN) are widely used for classification, and the training algorithm commonly used is the backpropagation (BP) algorithm. The major bottleneck faced in the backpropagation neural network training is in fixing the appropriate values for network parameters. The network parameters are initial weights, biases, activation function, number of hidden layers and the number of neurons per hidden layer, number of training epochs, learning rate, minimum error, and momentum term for the classification task. The objective of this work is to investigate the performance of 12 different BP algorithms with the impact of variations in network parameter values for the neural network training. The algorithms were evaluated with different training and testing samples taken from the three benchmark clinical datasets, namely, Pima Indian Diabetes (PID), Hepatitis, and Wisconsin Breast Cancer (WBC) dataset obtained from the University of California Irvine (UCI) machine learning repository.

Most Bariatric units perform Laparoscopic Roux-en-Y Gastric Bypass (LRYGB) and One Anastomosis Gastric Bypass (OAGB) for weight loss and metabolic purposes with satisfactory results and low complication profile.

AbstractAims:To describe the incidence and identify prognostic factors of central nervous system (CNS) adverse events (AEs) and any AEs (CNS, skin rash, or fracture) and evaluate the healthcare resource utilization (HCRU), direct medical costs, and therapy discontinuation associated with these AEs among non-metastatic prostate cancer (nmPC) patients who received secondary hormone therapies.Methods and results:nmPC patients who had initiated secondary hormonal therapy with enzalutamide, bicalutamide, or abiraterone ≥1 year after androgen deprivation therapy (ADT) were identified in the MarketScan database. Survival analyses were used to describe the incidence of CNS or any AEs. Annual HCRU and costs were compared across patient groups (CNS AE vs no CNS AE; any AE vs no AE) using propensity score weighted generalized linear models. Multivariate Cox proportional hazards models were used to identify AE predictors and compare risks of discontinuation.Results:The analysis included 532 patients who initiated secondary hormonal therapies, among whom 201 (38%) and 244 (46%) experienced a CNS AE and any AE, respectively. Median times to CNS AE and any AE from therapy initiation were 17.90 and 11.00 months, respectively. Predictors of any AE were any AE in the baseline period (≤6 months before starting therapy), Charlson Comorbidity Index (CCI) score (1 vs 0), surgical castration, and older age. Predictors of CNS AEs were CNS AE in the baseline period and CCI score (1 vs 0). CNS and any AEs were associated with significantly higher HCRU. CNS AEs were associated with significantly higher incremental total medical costs ($18,522). CNS AEs and any AEs significantly increased therapy discontinuation risk by 48% and 38%, respectively.Conclusions:AEs increase the economic burden and therapy discontinuation among nmPC patients receiving secondary hormonal therapies subsequent to ADTs. These patients should be carefully evaluated for AEs to reduce therapy discontinuation, HCRU, and direct medical costs.

Allergic rhinitis (AR) is an antigen-mediated inflammation of the nasal mucosa that might extend into the paranasal sinuses. Rhinorrhea, nasal obstruction or blockage, nasal itching, sneezing, and postnasal drip that reverse spontaneously or after treatment are symptoms of AR. Allergic conjunctivitis frequently accompanies AR. For diagnosis of AR, intradermal skin tests remain the gold standard. This paper presents a clinical decision-making system that assists the clinicians to diagnose whether a patient suffers from AR. Feature selection is done using a wrapper approach that employs genetic algorithm (GA) and extreme learning machine (ELM). The selected features are trained and tested using an ELM classifier. For experimenting, the outcome of the symptoms observed in 872 patients for diagnosing the presence or absence of AR has been used. The experimental result shows that the system has achieved an accuracy of 97.7%.

High-fat diet (HFD)–induced inflammation is associated with a variety of health risks. The systemic pentraxin serum amyloid P (SAP) inhibits inflammation. SAP activates the high-affinity IgG receptor Fcγ receptor I (FcγRI; CD64) and the lectin receptor dendritic cell–specific intercellular adhesion molecule-3–grabbing nonintegrin (DC-SIGN; CD209). Herein, we show that for mice on an HFD, injections of SAP and a synthetic CD209 ligand (1866) reduced HFD-increased adipose and liver tissue inflammation, adipocyte differentiation, and lipid accumulation in adipose tissue. HFD worsened glucose tolerance test results and caused increased adipocyte size; for mice on an HFD, SAP improved glucose tolerance test results and reduced adipocyte size. Mice on an HFD had elevated serum levels of IL-1β, IL-23, interferon (IFN)-β, IFN-γ, monocyte chemoattractant protein 1 [MCP-1; chemokine (C-C motif) ligand 2 (CCL2)], and tumor necrosis factor-α. SAP reduced serum levels of IL-23, IFN-β, MCP-1, and tumor necrosis factor-α, whereas 1866 reduced IFN-γ. In vitro, SAP, but not 1866, treated cells isolated from white fat tissue (stromal vesicular fraction) produced the anti-inflammatory cytokine IL-10. HFD causes steatosis, and both SAP and 1866 reduced it. Conversely, compared with control mice, SAP knockout mice fed on a normal diet had increased white adipocyte cell sizes, increased numbers of inflammatory cells in adipose and liver tissue, and steatosis; and these effects were exacerbated on an HFD. SAP and 1866 may inhibit some, but not all, of the effects of a high-fat diet.

Mammalian oocytes and eggs are transcriptionally quiescent and depend on post-transcriptional mechanisms for proper maturation. Post-transcriptional mRNA modifications comprise an important regulatory mechanism that can alter protein and miRNA recognition sites, splicing, stability, secondary structure, and protein coding. We discovered that fully grown mouse germinal vesicle oocytes and metaphase II eggs display abundant inosine mRNA modifications compared to growing oocytes from postnatal day 12 oocytes. These inosines were enriched in mRNA protein coding regions (CDS) and specifically located at the third codon base, or wobble position. Inosines, observed at lower frequencies in CDS of somatic tissues, were similarly enriched at the codon wobble position. In oocytes and eggs, inosine modifications lead primarily to synonymous changes in mRNA transcripts. Inosines may ultimately affect maternal mRNA stability by changing codon usage, thereby altering translational efficiency and translationally coupled mRNA degradation. These important observations advance our understanding of post-transcriptional mechanisms contributing to mammalian oocyte maturation.

Traditional/indigenous children’s games are yet to be appreciated as sites of critical engagement. The limited available literature on traditional children’s games is mainly focused on the deployment of traditional games in the teaching of conventional curriculum subjects. On the other hand, applied theatre projects appear to mainly rely on ‘foreign’ games. Curiously, there seems to be bias towards theatre games from North America, South America and Western Europe. We concede that, to an extent, the deployment of foreign games has revolutionized and radicalized applied theatre practice in Zimbabwe. However, there is hardly any serious scholarship that examines implications of infusing traditional children’s games within gender and disability discourse for child-centred development. This article addresses the extent to which applied theatre practitioners may appropriate traditional children’s games in the quest to deploy theatre as a site of critical engagement against pervasive hegemonies such as masculinity, neo-colonialism and ableism that may impede on human-/child-centred development. Drawing from Africana Womanism and Critical Disability Theory, we argue that although currently few applied theatre practitioners are deploying traditional children’s games, there is scope for appropriating and contextualizing traditional games in subverting imposed ableist and gendered hegemonies. Applied theatre projects can offer space to recover the rich intangible heritage that is embodied in traditional games. Having established that indigenous African children’s games affirm the material body in general, we recognize the potential of applied theatre contexts to act as sites of taping from the wisdom of the past in order to invent the future in which Africa/Zimbabwean children are proud of their bodily realities as centres of human growth and self-construction.

We hypothesized that burn location plays an important role in wound healing, mortality, and other outcomes and conducted the following study to test this multifold hypothesis. We conducted a study to retrospectively look at patients with burns =10% TBSA. Demographics, TBSA, partial/full thickness burns (PT/FT) in various wound locations, fluids, inhalation injury, mortality, ICU duration, and hospital duration were considered. Initial wound healing rates (%/d) were also calculated as a slope from the time of the first mapping of open wound size to the time of the third mapping of open wound size. Multivariate logistic regression and operating curves were used to measure mortality prediction performance. All values were expressed as median [interquartile range]. The mortality rate for 318 patients was 17% (54/318). In general, patients were 43 years [29, 58 years] old and had a TBSA of 25% [17, 39%], PT of 16% [10, 25%], and FT of 4% [0, 15%]. Between patients who lived and did not, age, TBSA, FT, 24-hour fluid, and ICU duration were statistically different (P< .001). Furthermore, there were statistically significant differences in FT head (0% [0, 0%] vs 0% [0, 1%], P= .048); FT anterior torso (0% [0, 1%] vs 1% [0, 4%], P< .001); FT posterior torso (0% [0, 0%] vs 0% [0, 4%], P< 0.001); FT upper extremities (0% [0, 3%] vs 2% [0, 11%], P< .001); FT lower extremities (0% [0, 2%] vs 6% [0, 17%], P< .001); and FT genitalia (0% [0, 0%] vs 0% [0, 2%], P< .001). Age, presence of inhalation injury, PT/FT upper extremities, and FT lower extremities were independent mortality predictors and per unit increases of these variables were associated with an increased risk for mortality (P< .05): odds ratio of 1.09 (95% confidence interval [CI] = 1.61–1.13; P< .001) for mean age; 2.69 (95% CI = 1.04–6.93; P= .041) for inhalation injury; 1.14 (95% CI = 1.01–1.27; P= .031) for mean PT upper extremities; 1.26 (95% CI = 1.11–1.42; P< .001) for mean FT upper extremities; and 1.07 (95% CI = 1.01–1.12; P= .012) for mean FT lower extremities. Prediction of mortality was better using specific wound locations (area under the curve [AUC], AUC of 0.896) rather than using TBSA and FT (AUC of 0.873). Graphs revealed that initial healing rates were statistically lower and 24-hour fluids and ICU length of stay were statistically higher in patients with FT upper extremities than in patients without FT extremities (P< .001). Burn wound location affects wound healing and helps predict mortality and ICU length of stay and should be incorporated into burn triage strategies to enhance resource allocation or stratify wound care.

Domain name system (DNS) tunneling attacks can bypass firewalls, which typically "trust" DNS transmissions by concealing malicious traffic in the packets trusted to convey legitimate ones, thereby making detection using conventional security techniques challenging. To address this issue, we propose a Lebesgue-2 regularized multilayer perceptron (L2R-MLP) algorithm for detecting DNS tunneling attacks. The DNS dataset was carefully curated from a publicly available repository, and relevant features, such as packet size and count, were selected using the recursive feature elimination technique. L2 regularization in the MLP classifier's hidden layers enhances pattern recognition during training, effectively countering the risk of overfitting. When evaluated against a benchmark MLP model, L2R-MLP demonstrated superior performance with 99.46% accuracy, 97.00% precision, a 97.00 F1-score, 99.95% recall, and an AUC of 89.00%. In comparison, the benchmark MLP achieved 92.53% accuracy, 96.00% precision, a 97.00 F1-score, 99.95% recall, and an AUC of 87.00%. This highlights the effectiveness of L2 regularization in improving predictive capabilities and model generalization for unseen instances.

In this work, a computer aided diagnosis (CAD) system to improve the diagnostic accuracy and consistency in image interpretation of pulmonary tuberculosis is proposed. The lung fields are segmented using region growing and edge reconstruction algorithms. Texture features are extracted from the diseased regions manifested as consolidations, cavitations and nodular opacities. A wrapper approach that combines cuckoo search optimisation and one-against-all SVM classifier is used to select optimal feature subset. Cuckoo search algorithm is implemented first using entropy and second without using entropy measure. Training is done with the selected features using one-against-all (SVM) classifier. Among the 98 features extracted from the diseased regions, 47 features are selected with entropy measure giving 92.77% accuracy. When entropy measure is not used, 51 features are selected giving 91.89% accuracy. From the results, it is inferred that selecting appropriate features for training the classifier has an impact on the classifier performance.

The aim of this study was to investigate whether current standards at our institution have effectively monitored resuscitations of severely burned patients during the first 48 hours postburn. Demographics, injuries assessed by TBSA and full thickness (FT), and resuscitation volumes (lactated Ringer’s [LR]) were compared for all patients and those who died or survived. Means and standard deviations of hourly indices (urinary output [UOP], lactate [LAC], base excess [BE]) vs LR were analyzed. Waveforms, four-quadrant concordance, and correlation were also employed to compare the trending abilities (hourly changes [?]) of aforementioned variables vs LR. A total of 203 patients were included in the analysis. Of these patients, 71 (35%) died, and 50 (25%) had inhalation injuries. Mean age and weight were 47 ± 19 years and 87 ± 18 kg, respectively. Mean TBSA burned was 41 ± 20%, with a mean FT of 18 ± 24%. Importantly, normalized waveform plots demonstrated the inability of UO, LAC, and BE to follow hourly changes in LR. Correlation of these variables was weak (r>>-1). This was confirmed by concordance plots. Slopes in all groups demonstrated that UOP was a better resuscitative monitor than LAC or BE. ?UOP responded to ?LR better in patients who survived than died. Reliance on hourly UOP as the sole index of optimal resuscitation is not supported. This study echoed the call for better resuscitation indices.