Reduce Mortality Rates Research Articles

Molecular diagnostics for clinical respiratory virus on a total integrated centrifugal microsystem using reverse transcription-loop-mediated isothermal amplification

Respiratory viruses are highly contagious agents that can cause endemic and epidemic infections in humans. Early detection of these viruses is crucial in preventing economic damage and reducing mortality rates. In this study, we present a total integrated genetic analyzer to perform a reverse transcription-loop-mediated isothermal amplification (RT-LAMP) assay for the simultaneous detection of 7 respiratory viruses (Influenza A H1N1 and H3N2, Influenza B, Respiratory syncytial virus A and B, Adenovirus, and COVID-19). The primer sets for the RT-LAMP assay were designed and evaluated in comparison with the RT-PCR assay using clinical samples, confirming high specificity and efficiency. The entire process of viral RNA extraction, reagent mixing, gene amplification, and detection was completed on the device in 1 hr 20 min. The constructed portable diagnostic instrument is equipped with a rotary motor, two sets of peltier heaters, a fluorescence detector, and a touch screen for inputting experimental parameters and displaying result. The proposed point-of-care (POC) diagnostic platform correctly analyzed a total of 21 clinical samples (3 for each of the 7 viruses). The limit-of-detection (LOD) for Influenza A subtype H3N2 was 101 pfu/mL, which demonstrates the high performance of our proposed centrifugal microsystem for on-site molecular diagnostics in medical centers.

Photocatalytic degradation of gentamicin using TiO2 nanoparticle driven by UV light irradiation

Antibiotics have contributed to enhance life expectancy in underdeveloped countries by reducing mortality rate, but the hazards connected with antibiotic pollution are largely harming people. The photocatalytic degradation of gentamicin utilizing Titanium oxide nanoparticles (TiO2nps) using visible and UV light has been studied. TiO2nps was synthesized using the co-precipitation method and calcined at 600 °C. XRD study confirmed the phase formation and Raman study confirmed the structure of TiO2nps. UV–Vis’s absorption study was performed in which a broad peak was obtained between 300 and 600 nm. The degradation percentage for the photocatalytic study was found to be 51 %, 84 %, and 95 % under dark, visible light, and UV light, respectively. The pseudo-first order reaction rate constant was calculated for the sample exposed to UV light, and was found to be 0.0158. In the cyclic study, high degradation percentage of more than 80 % was obtained even after four cycle of degradation.

Fluid Optimisation in Emergency Laparotomy (FLO-ELA) Trial: study protocol for a multi-centre randomised trial of cardiac output-guided fluid therapy compared to usual care in patients undergoing major emergency gastrointestinal surgery

IntroductionPostoperative morbidity and mortality in patients undergoing major emergency gastrointestinal surgery are a major burden on healthcare systems. Optimal management of perioperative intravenous fluids may reduce mortality rates and improve outcomes from surgery. Previous small trials of cardiac-output guided haemodynamic therapy algorithms in patients undergoing gastrointestinal surgery have suggested this intervention results in reduced complications and a modest reduction in mortality. However, this existing evidence is based mainly on elective (planned) surgery, with little evaluation in the emergency setting. There are fundamental clinical and pathophysiological differences between the planned and emergency surgical setting which may influence the effects of this intervention. A large definitive trial in emergency surgery is needed to confirm or refute the potential benefits observed in elective surgery and to inform widespread clinical practice.MethodsThe FLO-ELA trial is a multi-centre, parallel-group, open, randomised controlled trial. 3138 patients aged 50 and over undergoing major emergency gastrointestinal surgery will be randomly allocated in a 1:1 ratio using minimisation to minimally invasive cardiac output monitoring to guide protocolised administration of intra-venous fluid, or usual care without cardiac output monitoring. The trial intervention will be carried out during surgery and for up to 6 h postoperatively. The trial is funded through an efficient design call by the National Institute for Health and Care Research Health Technology Assessment (NIHR HTA) programme and uses existing routinely collected datasets for the majority of data collection. The primary outcome is the number of days alive and out of hospital within 90 days of randomisation. Participants and those delivering the intervention will not be blinded to treatment allocation. Participant recruitment started in September 2017 with a 1-year internal pilot phase and is ongoing at the time of publication.DiscussionThis will be the largest contemporary randomised trial examining the effectiveness of perioperative cardiac output-guided haemodynamic therapy in patients undergoing major emergency gastrointestinal surgery. The multi-centre design and broad inclusion criteria support the external validity of the trial. Although the clinical teams delivering the trial interventions will not be blinded, significant trial outcome measures are objective and not subject to detection bias.Trial registrationISRCTN 14729158. Registered on 02 May 2017.

Heart Disease Prediction using Machine Learning

Heart disease is a significant global health concern, accounting for a substantial number of deaths worldwide. Early prediction and detection of heart disease play a pivotal role in improving patient outcomes and reducing mortality rates. Machine learning techniques have demonstrated considerable potential in accurately predicting heart disease based on patient data. In this research paper, we propose a novel approach to heart disease prediction using machine learning algorithms, with a particular focus on creating a user-friendly graphical user interface (GUI) for enhanced accessibility and ease of use. The proposed approach leverages a diverse dataset encompassing demographic information, medical history, laboratory results, and diagnostic tests, providing a comprehensive view of a patient's health status. Multiple state-of-the-art machine learning algorithms, including logistic regression, support vector machines, random forests, and artificial neural networks, are employed to build robust prediction models. These models are trained, validated, and evaluated using appropriate performance metrics to ensure accuracy and reliability. To facilitate practical implementation, a user-friendly GUI is designed to provide an intuitive interface for healthcare professionals and individuals without extensive programming or machine learning expertise.

A Comparative Study on Prediction of Endometriosis Causing Infertility Using Machine Learning Techniques: in Detail

The purpose of this study is to utilize Artificial Intelligence to analyse and predict endometriosis problem in women. All traditional methods are used before to develop or to predict the likelihood of endometriosis based on the symptoms presented. By identifying the symptoms of endometriosis, the machine learning algorithms can determine the type of endometriosis and the appropriate course of action for patients. This technology can be used to educate women globally on the signs and symptoms of endometriosis and help them take preventive measures to avoid this deadly disease. The results of this research demonstrate the potential of advanced technology to revolutionize healthcare by providing early detection and treatment options for endometriosis. In areas with limited access to medical care, this tool can aid in identifying ovarian cancer and reducing mortality rates. By detecting and diagnosing endometriosis at an early stage, this program can play a significant role in promoting women's health and wellbeing. The methodology proposed in this study produces classification results that are on par with cutting-edge deep learning techniques. In addition, the methodology provides visual explanations that offer valuable insights into the inner workings of each model and enhance the accuracy and reliability of the predictions.

Evaluation of Patients with Acute Respiratory Distress Syndrome Followed on Mechanical Ventilator in a Tertiary Pediatric Intensive Care and the Factors That May Be Associated with Death in These Patients.

We aimed to evaluate the clinical, demographic, and laboratory characteristics of the patients followed up with pediatric acute respiratory distress syndrome in our pediatric intensive care unit and to determine the factors that have an effect on the outcomes. The medical records of 40 patients with acute respiratory distress syndrome who were followed up on mechanical ventilators in the pediatric intensive care unit of Adıyaman University were retrospectively scanned. From the medical records, the demographic data, clinical features, and laboratory characteristics were recorded. Eighteen of the patients were female and 22 were male. The mean age was 45.25 ± 56.63 months. A total of 27 (67.5%) of the patients were classified as pulmonary and 13 (32.5%) as extrapulmonary acute respiratory distress syndrome. Sixteen (40%) patients were followed in pressure-controlled mode only, 2 (5%) patients in volume-controlled mode only, and 22 (55%) patients in alternate modes. A total of 17 (42.5%) patients died. The median pediatric index of mortality, pediatric index of mortality-II, pediatric risk of mortality, and pediatric logistic organ dysfunction score values of the surviving patients were significantly lower than the dead patients. Median aspartate aminotransferase (P = .003) and lactate dehydrogenase (P = .008) values were found to be significantly higher in patients who died, while median pH values (P = .049) were found to be lower. The median length of stay in pediatric intensive care unit and duration of mechanical ventilators were significantly shorter in patients who died. Also, the median pediatric index of mortality, pediatric index of mortality-II, pediatric risk of mortality, and pediatric logistic organ dysfunction values of pulmonary acute respiratory distress syndrome patients were significantly lower than those of extrapulmonary acute respiratory distress syndrome patients. Despite advances in follow-up and management, mortality due to acute respiratory distress syndrome is still high. Mechanical ventilator duration, length of stay in pediatric intensive care unit, some mechanical ventilator parameters, mortality scores, and laboratory tests were associated with mortality. Alternatively, mechanical ventilator applications may reduce mortality rates.

Exploring the attitudes and practices of female doctors towards cervical cancer screening in primary health care centers.

Cervical cancer is a significant cause of female mortality worldwide, and early detection through regular screening is crucial for reducing mortality rates. However, in developing countries, the uptake of Pap smear tests (PST) is low, mostly due to cultural and social factors and a lack of knowledge. This cross-sectional study assessed knowledge, attitude, and practice of cervical cancer screening among practitioners working at primary healthcare centers in Saudi Arabia. Furthermore, the study aimed to identify the potential barriers that prevent female physicians from performing cervical cancer screening tests. A self-administrated, well-structured questionnaire was used to survey 95 female physicians, including residents, specialists, and consultants in several primary health care centers (PHCCs) in Jeddah managed by the Ministry of Health during September 2019. The results showed that 80% of participants knew about cervical cancer, and 97.8% were aware that PST is a screening tool. However, only 47% advised female patients to get tested for cervical cancer. The factors identified as barriers to test uptake included asymptomatic females (34%), lack of time on the part of the practitioner (24%), and a lack of evidence for risk factors (23%). Additionally, only 42.2% of the participating physicians had undergone a Pap smear test themselves. The study highlights the need for further research to assess HPV status in the population and explore the correlation between circumcision and cervical cancer, as well as polygamy and cervical cancer. The findings suggest that while a good level of knowledge about cervical cancer exists, there is a need to improve compliance with cervical cancer screening guidelines among female physicians in Saudi Arabia.

Treatment options for patients with severe COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic has affected the world for over 3 years. Treatment options have improved substantially during this period, including antiviral drugs, antibody drugs, immune-based agents, and vaccination. While these improvements have reduced mortality rates in patients with COVID-19, some patients still develop severe illness. In this review, we aimed to provide an overview of treatments for patients with severe COVID-19 from study reports and clinical experience. We discussed the treatments from two perspectives: respiratory care and drug treatments. In the respiratory care section, we discussed the usefulness of high-flow nasal cannula therapy and non-invasive ventilation as an alternative to invasive ventilation. In the drug treatments section, we focused on three classes for severe COVID-19 treatment: antiviral drugs, immune-based agents, and anticoagulation therapy. We did not discuss antibody drugs and vaccination, as they are not used for severe COVID-19 treatment.

Hybrid Optimization based Feature Selection with DenseNet Model for Heart Disease Prediction

The prevalence of cardiovascular diseases (CVD) makes it one of the leading reasons of death worldwide. Reduced mortality rates may result from early detection of CVDs and their potential prevention or amelioration. Machine learning models are a promising method for identifying risk variables. In order to make accurate predictions about cardiovascular illness, we would like to develop a model that makes use of transfer learning. Our proposed model relies on accurate training data, which was generated by careful Data Collecting, Data Pre-processing, and Data Transformation procedures.

Deep Learning Paradigms for Existing and Imminent Lung Diseases Detection: A Review

Diagnosis of lung diseases like asthma, chronic obstructive pulmonary disease, tuberculosis, cancer, etc., by clinicians rely on images taken through various means like X-ray and MRI. Deep Learning (DL) paradigm has magnified growth in the medical image field in current years. With the advancement of DL, lung diseases in medical images can be efficiently identified and classified. For example, DL can detect lung cancer with an accuracy of 99.49% in supervised models and 95.3% in unsupervised models. The deep learning models can extract unattended features that can be effortlessly combined into the DL network architecture for better medical image examination of one or two lung diseases. In this review article, effective techniques are reviewed under the elementary DL models, viz. supervised, semi-supervised, and unsupervised Learning to represent the growth of DL in lung disease detection with lesser human intervention. Recent techniques are added to understand the paradigm shift and future research prospects. All three techniques used Computed Tomography (C.T.) images datasets till 2019, but after the pandemic period, chest radiographs (X-rays) datasets are more commonly used. X-rays help in the economically early detection of lung diseases that will save lives by providing early treatment. Each DL model focuses on identifying a few features of lung diseases. Researchers can explore the DL to automate the detection of more lung diseases through a standard system using datasets of X-ray images. Unsupervised DL has been extended from detection to prediction of lung diseases, which is a critical milestone to seek out the odds of lung sickness before it happens. Researchers can work on more prediction models identifying the severity stages of multiple lung diseases to reduce mortality rates and the associated cost. The review article aims to help researchers explore Deep Learning systems that can efficiently identify and predict lung diseases at enhanced accuracy.

Predicting COVID-19 Induced Mortality Utilizing Vaccination Status Data

COVID-19 is a prevalent pandemic that has caused of millions of mortalities. While medical and statistical research have proven the effectiveness of COVID vaccines to reduce mortality rate in specific areas. This study aims to explore the quantitative effect of vaccine in preventing in the global level. Mortality and vaccine data was acquired from Kaggle, which summarized related data from various sources. Multiple linear regression, quasi-Poisson regression, LASSO regression, and random forest are applied to the model to analyze and predict the effect of vaccine on mortality. The adjusted R square value of these four models is 0.6670, 0.6863, 0.4901, and 0.7152 respectively. The residual boxplots of all four models show that LASSO model sometimes make extremely inaccurate predictions, and the random forest model is the most accurate model, which matches the comparison result of the adjusted R square values. Therefore, the random forest model can be potentially used to predict future COVID-related deaths based on vaccine-related data.

Progressive growing of Generative Adversarial Networks for improving data augmentation and skin cancer diagnosis

Early melanoma diagnosis is the most important factor in the treatment of skin cancer and can effectively reduce mortality rates. Recently, Generative Adversarial Networks have been used to augment data, prevent overfitting and improve the diagnostic capacity of models. However, its application remains a challenging task due to the high levels of inter and intra-class variance seen in skin images, limited amounts of data, and model instability. We present a more robust Progressive Growing of Adversarial Networks based on residual learning, which is highly recommended to ease the training of deep networks. The stability of the training process was increased by receiving additional inputs from preceding blocks. The architecture is able to produce plausible photorealistic synthetic 512 × 512 skin images, even with small dermoscopic and non-dermoscopic skin image datasets as problem domains. In this manner, we tackle the lack of data and the imbalance problems. Additionally, the proposed approach leverages a skin lesion boundary segmentation algorithm and transfer learning to enhance the diagnosis of melanoma. Inception score and Matthews Correlation Coefficient were used to measure the performance of the models. The architecture was evaluated qualitatively and quantitatively through the use of an extensive experimental study on sixteen datasets, illustrating its effectiveness in the diagnosis of melanoma. Finally, four state-of-the-art data augmentation techniques applied in five convolutional neural network models were significantly outperformed. The results indicated that a bigger number of trainable parameters will not necessarily obtain a better performance in melanoma diagnosis.

Evaluation of the pathogenicity of Pseudomonas aeruginosa isolates to strains of mice under stressful conditions

Pseudomonas aeruginosa (Pa) is an opportunistic pathogen that threatens the health, especially in immunocompromised individuals. Pseudomonas aeruginosa survives everywhere, including in a shuttle of spaceflights. Survival and starvation of Pa in water probably increase or decrease the degree of pathogenicity or virulence, but it is not well discovered. To promote our understanding of the mechanisms of Pa long-term survival in water, investigations on survival kinetics and its pathogenicity to mice are under investigation in our laboratory. Data has shown that under starvation conditions in water, Pa can lead to (i) survival for over 15 years in water; (ii) distinct changes in colony morphology, including loss of pigmentation. This study also aimed to determine the pathogenicity of clinical and environmental isolates of Pa in water under starved conditions. We hypothesized that the starvation of Pa isolates in water results in different survival curves, morphological, that may enhance pathogenicity in mice. Mice infected with starved isolates showed a lesser death rate than non-starved isolates. This project compared the susceptibility of BALB/c, C57BL/6J, and DBA/2 J in lung infection. Our data shows the vulnerability of DBA/2J was high compared to BALB/c or C57BL/6J. Hindlimb-unloading increased the death rate in DBA/2J compared to BALB/c strains. Lung infection of different Pa isolates shows a differential death rate, with the highest death rate caused by non-starved isolates in mice. Overall, long-term starvation of Pa isolates exits may lead to a reduced mortality rate of mice.

Impact of cavotricuspid isthmus ablation for typical atrial flutter and heart failure in the elderly-results of a retrospective multi-center study.

While in the CASTLE-AF trial, in patients with atrial fibrillation and heart failure with reduced ejection fraction, interventional therapy using pulmonary vein isolation was associated with outcome improvement, data on cavotricuspid isthmus ablation (CTIA) in atrial flutter (AFL) in the elderly is rare. We included 96 patients between 60 and 85 years with typical AFL and heart failure with reduced or mildly reduced ejection fraction (HFrEF/HFmrEF) treated in two medical centers. 48 patients underwent an electrophysiological study with CTIA, whereas 48 patients received rate or rhythm control and guideline-compliant heart failure therapy. Patients were followed up for 2 years, with emphasis on left ventricular ejection fraction (LVEF) over time. Primary endpoints were cardiovascular mortality and hospitalization for cardiac causes. Patients with CTIA showed a significant increase in LVEF after 1 (p < 0.001) and 2 years (p < 0.001) in contrast to baseline LVEF. Improvement of LVEF in the CTIA group was associated with significantly lower 2-year mortality (p = 0.003). In the multivariate regression analysis, CTIA remained the relevant factor associated with LVEF improvement (HR: 2.845 CI:95% 1.044-7.755; p = 0.041). Elderly patients (≥ 70 years) further benefited from CTIA, since they showed a significantly reduced rehospitalization (p = 0.042) and mortality rate after 2 years (p = 0.013). CTIA in patients with typical AFL and HFrEF/HFmrEF was associated with significant improvement of LVEF and reduced mortality rates after 2 years. Patient age should not be a primary exclusion criterion for CTIA, since patients ≥70 years also seem to benefit from intervention in terms of mortality and hospitalization.

Mortality Trends in Geriatric Proximal Femoral Fracture Treatments After National Payor Policy Changes: A National Study.

Proximal femoral fractures (PFFs) are a major medical event in an elderly's life. The extent of conservative treatment is poorly evaluated in Western health systems. This study retrospectively examines a national cohort of patients older than 65 years with PFFs treated by early surgery (ES) (<48h), delayed surgery (DS) (>48h), and conservative treatment (COT) over the past decade (2010 to 2019). The study cohort included 38,841 patients; 18.4% were 65 to 74 years, 41.1% were 75 to 84 years, and 40.5% were older than 85 years; 68.5% were female. ES rose from 68.4% in 2013 to 85% in 2017 (P < 0.0001). COT dropped from 8.2% in 2010 to 5.2% in 2019 (P < 0.0001). Level I trauma centers chose 2.3 times less COT (7.75% in 2010 decreased to 3.37% in 2019) while regional hospitals chose COT only 1.4 times less over the years (P < 0.001). Hospitalization periods differed: 6.3 ± 0.6d for COT, 8.6 ± 0.3d for ES, and 12 ± 0.4d for DS (P < 0.001), and the in-hospital mortality rates were 10.5%, 2%, and 3.6%, respectively (P < 0.0001). One-year mortality rates decreased for ES only (P < 0.001). ES rose from 58.1% in 2010 to 84.9% in 2019 (P = 0.00002). COT is diminishing throughout the Israeli health system, from 8.2% in 2010 to 5.2% in 2019. Tertiary hospitals consistently practice less COT than regional hospitals (P < 0.001), probably related to surgeons' and anesthetists' appraisal of the patient's medical status and demand. COT had the shortest hospitalization period but carried the highest in-hospital mortality rates (10.5%). The mild difference in out-of-hospital mortality between the COT and DS groups suggests similar patient characteristics that require additional investigation. In conclusion, more PFFs are treated within 48h with a reduced mortality rate, and the 1-year mortality has improved for ES only. Treatment preferences vary between tertiary and regional hospitals.

Analysis of the Kampung Family Planning (KB) Program Pucangsawit Village, Jebres District and Sondakan Village, Laweyan District, Surakarta City

Population growth is one of the social problems, in dealing with population growth the governmenthas a Family Planning Program (KB) . This program aims to improve public welfare and health,reduce birth rates, raise the nation's standard of living, and reduce mortality rates in pregnant womenand infants. In its implementation, the government forms the KB Village Program which is located atthe village level area. The research was conducted in KB Pucangsawit Village and KB SondakanVillage. This area was chosen based on the consideration of population growth which increases everyyear so that it needs to be studied in the implementation of the Kampung KB Program. The purpose ofthis study was to analyze the implementation of the Kampung KB program in the two regions and toanalyze the supporting and inhibiting factors found in the implementation of the Kampung KBProgram. This study uses a survey method and uses descriptive, quantitative and SWOT analysis.From the research it shows that the Kampung KB Pucangsawit has an optimal classification while theKampung KB Sondakan has a less than optimal classification. While the education factor af ects theimplementation of the Kampung KB Program 97.69%: 92.11%, then the influence of income is52.15%: 30.20%. The SWOT analysis shows the results of quadrant 1 so that there is an SO strategyto use program opportunities, so as to minimize inhibiting factors and maximize supporting factors.

Abstract 995: Evaluation of plasma cytokines and chemokines as potential biomarkers for colorectal cancer detection

Abstract Despite evidence suggesting that current Colorectal Cancer (CRC) screening strategies reduce mortality rates, screening compliance is suboptimal; for this, our study aimed to evaluate the discriminatory potential of plasma cytokine and chemokine levels for CRC detection in plasma samples in a Hispanic subpopulation. Using a retrospective case-control study design, we evaluated plasma samples in 20 CRC cases and 20 age-gender matched controls from the Puerto Rico Familial Colorectal Cancer Registry (PURIFICAR). BD CBA Human Th1/Th2/Th17 Cytokine and Human Chemokine kits were used to prepare plasma samples for flow cytometric analysis (Becton-Dickinson &amp; Co). Mann-Whitney-U test was used to detect differences between biomarkers and case-control groups. Measures of diagnostic accuracy were estimated for comparisons according to CRC disease stage. We compared multivariable models to determine the best CRC case-classifier. The median levels of six of the cytokines evaluated (IL-2, IL-4, IL-6, IL-10, TNF, and IL-17A) and one chemokine (CXCL10-/IP-10) significantly differed between cases and controls (P&amp;lt;0.05). ROC plots and logistic regression analysis showed that these seven biomarkers displayed good to excellent accuracy (AUC: 0.6-0.9). Multivariate analysis showed that the best-case classifying model included the seven statistically significant markers. Our findings suggest that discriminatory biomarker levels between CRC cases and controls observed show their great potential as targets in novel blood-based screening tests for CRC. The development of a non-invasive CRC screening test will provide an alternative to increasing adherence, early detection, survivorship, and a tool for a population-based prevention strategy approach. Citation Format: Elba V. Caraballo, Hilmaris Centeno-Girona, Yilda Macias, Madeline M. Mártir Ocasio, Marcia R. Cruz-Correa. Evaluation of plasma cytokines and chemokines as potential biomarkers for colorectal cancer detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 995.

The role of neutrophil extracellular traps (NETs) in the immunopathogenesis of severe COVID-19: potential immunotherapeutic strategies regulating NET formation and activity

The role of neutrophil granulocytes (NG) in the pathogenesis of COVID-19 is associated with the NG recruitment into inflammatory foci, activation of their functions and enhanced formation of neutrophil extracellular networks (NETs). In this review, we analyzed a large body of scientific literature devoted to the features of developing NETs, their role in the COVID-19 pathogenesis, a role in emerging immunothrombosis, vasculitis, acute respiratory distress syndrome, cytokine storm syndrome, and multi-organ lesions. Convincing data are presented clearly indicating about a profound role of NETs in the COVID-19 immunopathogenesis and associated severe complications resulting from intensified inflammation process, which is a key for the course of SARS-CoV-2 virus infection. The presented role of NGs and NETs, along with that of other immune system cells and pro-inflammatory cytokines, is extremely important in understanding development of overactive immune response in severe COVID-19. The scientific results obtained available now allow to identify an opportunity of regulatory effects on hyperactivated NGs, NETosis at various stages and on limiting a negative impact of pre-formed NETs on various tissues and organs. All the aforementioned data should help in creating new, specialized immunotherapy strategies designed to increase the odds of survival, reduce severity of clinical manifestations in COVID-19 patients as well as markedly reduce mortality rates. Currently, it is possible to use existing drugs, while a number of new drugs are being developed, the action of which can regulate NG quantity, positively affect NG functions and limit intensity of NETosis. Continuing research on the role of hyperactive NG and NETosis as well as understanding the mechanisms of regulating NET formation and restriction in severe COVID-19, apparently, are of high priority, because in the future the new data obtained could pave the basis for development of targeted approaches not only for immunotherapy aimed at limiting education and blocking negative effects already formed NETs in severe COVID-19, but also for immunotherapy, which could be used in combination treatment of other netopathies, primarily autoimmune diseases, auto-inflammatory syndromes, severe purulent-inflammatory processes, including bacterial sepsis and hematogenous osteomyelitis.

Predicting the mortality of patients with Covid-19: A machine learning approach.

Infection with Covid-19 disease can lead to mortality in a short time. Early prediction of the mortality during an epidemic disease can save patients'lives through taking timely and necessary care interventions. Therefore, predicting the mortality of patients with Covid-19 using machine learning techniques can be effective in reducing mortality rate in Covid-19. The aim of this study is to compare four machine-learning algorithm for predicting mortality in Covid-19 disease. The data of this study were collected from hospitalized patients with COVID-19 in fivehospitals settings in Tehran (Iran). Database contained 4120 records, about 25% of which belonged to patients who died due to Covid-19. Each record contained 38 variables. Four machine-learning techniques, including random forest (RF), regression logistic (RL), gradient boosting tree (GBT), and support vector machine (SVM) were used in modeling. GBT model presented higher performance compared to other models (accuracy 70%, sensitivity 77%, specificity 69%, and the ROC area under the curve 0.857). RF, RL, and SVM models with the ROC area under curve 0.836, 0.818, and 0.794 were in the secondand third places. Considering the combination of multiple influential factors affecting death Covid-19 can help in early prediction and providing a better care plan. In addition, using different modeling on data can be useful for physician in providing appropriate care.

An Initial Study on the Use of Machine Learning and Radio Frequency Identification Data for Predicting Health Outcomes in Free-Range Laying Hens.

Maintaining the health and welfare of laying hens is key to achieving peak productivity and has become significant for assuring consumer confidence in the industry. Free-range egg production systems represent diverse environments, with a range of challenges that undermine flock performance not experienced in more conventional production systems. These challenges can include increased exposure to parasites and bacterial or viral infection, along with injuries and plumage damage resulting from increased freedom of movement and interaction with flock-mates. The ability to forecast the incidence of these health challenges across the production lifecycle for individual laying hens could result in an opportunity to make significant economic savings. By delivering the opportunity to reduce mortality rates and increase egg laying rates, the implementation of flock monitoring systems can be a viable solution. This study investigates the use of Radio Frequency Identification technologies (RFID) and machine learning to identify production system usage patterns and to forecast the health status for individual hens. Analysis of the underpinning data is presented that focuses on identifying correlations and structure that are significant for explaining the performance of predictive models that are trained on these challenging, highly unbalanced, datasets. A machine learning workflow was developed that incorporates data resampling to overcome the dataset imbalance and the identification/refinement of important data features. The results demonstrate promising performance, with an average 28% of Spotty Liver Disease, 33% round worm, and 33% of tape worm infections correctly predicted at the end of production. The analysis showed that monitoring hens during the early stages of egg production shows similar performance to models trained with data obtained at later periods of egg production. Future work could improve on these initial predictions by incorporating additional data streams to create a more complete view of flock health.