Reduce Death Rates Research Articles

Exploring molecular moieties on carbonized polymer dots from flavonoid glycosides with activity against enterovirus A71

In recent years, numerous reports have revealed that hesperetin (Hsp) and its glycoside form, hesperidin (Hsd), exhibit significant antioxidant, anti-inflammatory, antibacterial, and antiviral activities. However, high concentrations of Hsp or Hsd are required to achieve in vivo therapeutic efficacy, since the accompanying toxicity has limited their use practical applications. To enhance bioactivity while reducing the potential associated toxicity, we synthesized carbonized polymer dots (CPDs) from hesperetin (Hsp) or hesperidin (Hsd) via mild pyrolysis and compared their antiviral activities with those of their raw precursors against enterovirus A71 (EV-A71). Our results showed that while Hsp and Hsd exhibited limited antiviral activity to suppress EV-A71-induced cytotoxicity in rhabdomyosarcoma cells, the CPDs possessed excellent antiviral activity with median effective concentration values of 44.2 and 17.7 μg mL−1, respectively, demonstrating improved antiviral effects compared to Hsp and Hsd (each >200 μg mL−1). Hsd-CPDs showed superior inhibition of EV-A71 infection compared to Hsp-CPDs, as shown by the >8-fold higher selectivity index. Molecular docking further suggests that apocynin and guaiacol moieties on the surface of the Hsd-CPDs could bind to the hydrophobic pocket of viral protein 1 of EV-A71, thus inhibiting viral attachment. We also demonstrated that Hsd-CPDs exert multiple mechanisms, including blocking viral entry and suppressing oxidative stress that triggered viral replication and translation. More importantly, Hsd-CPDs substantially alleviate the clinical symptoms and reduce death rates associated with EV-A71 infection in neonatal mice. Our results reveal the superior therapeutic effects of Hsd-CPDs against EV-A71 could be a potential antiviral agent worthy of future clinical investigation.

Automated Cardioailment Identification and Prevention by Hybrid Machine Learning Models.

Accurate prediction of cardiovascular disease is necessary and considered to be a difficult attempt to treat a patient effectively before a heart attack occurs. According to recent studies, heart disease is said to be one of the leading origins of death worldwide. Early identification of CHD can assist to reduce death rates. When it comes to prediction using traditional methodologies, the difficulty arises in the intricacy of the data and relationships. This research is aimed at applying recent machine learning technology to identify heart disease from past medical data to uncover correlations in data that can greatly improve the accuracy of prediction rates using various machine learning models. Models have been implemented using naive Bayes, random forest algorithms, and the combinations of two models such as naive Bayes and random forest methods. These methods offer numerous attributes associated with heart disease. This proposed system foresees the chance of rising heart disease. The proposed system uses 14 parameters such as age, sex, quick blood sugar, chest discomfort, and other medical parameters which are used in the proposed system. Our proposed systems find the probability of developing heart disease in percentages as well as the accuracy level (accuracy of 93%). Finally, this proposed method will support the doctors to analyze the heart patients competently.

Detecting breast cancer using novel mask R‐CNN techniques

AbstractBreast cancer is more common in women and the mortality rate also increases in recent days. Early detection of breast cancer is reduces the severity of the disease to some extent. Various image processing and classification techniques imposed on a particular image to detect and diagnose the breast cancer clearly. For early detection, an efficient methodology is needed. To reduce death rate, accurate discovery of the disease should be efficiently implemented. The mask R‐CNN is used for segmentation to identify abnormalities and ensemble CNN is used to classify the benign and malignant tumour from the given mammographic breast cancer image. The input is fed into the enhanced fuzzy based median filter which is used to remove speckle noises which in turn increases the clarity of images. The noise‐free images are fed into the proposed mask R‐CNN for segmentation. It is an effective framework for segmentation of medical images. The classification process is then given into ensemble based CNN classifier for prediction of mammogram images. The ensemble based CNN is proposed for the classification of mammograms as benign and malignant. The proposed method focus on the optimization which is performed to minimize the memory requirements and running time while maintaining the high performance of the classifier. The performance metrics such as accuracy, precision, f‐measure, and recall is evaluated to measure the efficacy of the proposed design. Using MIAS and DDSM dataset the performance metrics has been evaluated and compared with existing approaches. The results shows that the performance of the proposed method shows higher efficiency.

Using Machine Learning to Predict Heart Disease

Heart Disease has become one of the most leading cause of the death on the planet and it has become most life-threatening disease. The early prediction of the heart disease will help in reducing death rate. Predicting Heart Disease has become one of the most difficult challenges in the medical sector in recent years. As per recent statistics, about one person dies from heart disease every minute. In the realm of healthcare, a massive amount of data was discovered for which the data-science is critical for analyzing this massive amount of data. This paper proposes heart disease prediction using different machine-learning algorithms like logistic regression, naïve bayes, support vector machine, k nearest neighbor (KNN), random forest, extreme gradient boost, etc. These machine learning algorithm techniques we used to predict likelihood of person getting heart disease on the basis of features (such as cholesterol, blood pressure, age, sex, etc. which were extracted from the datasets. In our research we used two separate datasets. The first heart disease dataset we used was collected from very famous UCI machine learning repository which has 303 record instances with 14 different attributes (13 features and one target) and the second dataset that we used was collected from Kaggle website which contained 1190 patient’s record instances with 11 features and one target. This dataset is a combination of 5 popular datasets for heart disease. This study compares the accuracy of various machine learning techniques. In our research, for the first dataset we got the highest accuracy of 92% by Support Vector Machine (SVM). And for the second dataset, Random Forest gave us the highest accuracy of 94.12%. Then, we combined both the datasets which we used in our research for which we got the highest accuracy of 93.31% using Random Forest.

Hyperparameter Tuning Bidirectional Gated Recurrent Unit Model for Oral Cancer Classification

Oral Squamous Cell Carcinoma (OSCC) is a type of Head and Neck Squamous Cell Carcinoma (HNSCC) and it should be diagnosed at early stages to accomplish efficient treatment, increase the survival rate, and reduce death rate. Histopathological imaging is a wide-spread standard used for OSCC detection. However, it is a cumbersome process and demands expert’s knowledge. So, there is a need exists for automated detection of OSCC using Artificial Intelligence (AI) and Computer Vision (CV) technologies. In this background, the current research article introduces Improved Slime Mould Algorithm with Artificial Intelligence Driven Oral Cancer Classification (ISMA-AIOCC) model on Histopathological images (HIs). The presented ISMA-AIOCC model is aimed at identification and categorization of oral cancer using HIs. At the initial stage, linear smoothing filter is applied to eradicate the noise from images. Besides, MobileNet model is employed to generate a useful set of feature vectors. Then, Bidirectional Gated Recurrent Unit (BGRU) model is exploited for classification process. At the end, ISMA algorithm is utilized to fine tune the parameters involved in BGRU model. Moreover, ISMA algorithm is created by integrating traditional SMA and Chaotic Oppositional Based Learning (COBL). The proposed ISMA-AIOCC model was validated for performance using benchmark dataset and the results pointed out the supremacy of ISMA-AIOCC model over other recent approaches.

Attitudes of Yoga Practitioners Toward COVID-19 Virus Vaccination: A Cross-Sectional Survey in Germany.

To avoid severe courses of COVID-19 infections and reduce death rates, vaccination against the SARS-CoV-1 virus was considered an essential strategy in fighting the pandemic. However, some yoga practitioners reject such vaccinations and assume that their yoga practices protect them. We therefore aimed to analyze how many yoga practitioners were vaccinated, their reasons for being vaccinated, and the influence of the ethical principles of yoga (yamas/niyamas) on these decisions. In a cross-sectional survey in summer 2021, we enrolled 1,545 yoga practitioners (86% women; mean age 51.1 ± 10.9 y). The majority of participants were already vaccinated (66%), and their percentage corresponded to that of the general population. Those who were not willing to get vaccinated scored significantly higher on the yama/niyama factors Contentment/Self-Reflection/Devotion and Surrender and Non-Possessiveness. Depending on the centrality of the yamas/niyamas in their lives, yoga participants differed on their vaccination decisions, but they did not relevantly differ on their pro-social reasons (protection of groups at risk, protection of family) when they were already vaccinated. This assumed protection against severe courses of the COVID-19 infection was higher in the nonvaccinated compared to the vaccinated individuals (Cohen's d = 0.99). This conviction was related to the niyama factor Contentment/Self-Reflection/Devotion and Surrender. Thus, in the yoga schools and other places of yoga practice the relevance of vaccination to also protect others should be discussed, and the consequences of following the yamas and niyamas for the sake of others should be clarified.

Identification and Validation of Serum CST1 as a Diagnostic Marker for Differentiating Early-Stage Non-Small Cell Lung Cancer from Pulmonary Benign Nodules.

BackgroundEffective means for early diagnosis are imperative to reduce death rate of non-small cell lung cancer (NSCLC) patients. We aimed to find out high-performance serologic markers to distinguish early-stage NSCLC patients from benign pulmonary nodule patients and healthy controls (HC). Cystatin-SN (CST1) is an active cysteine protease inhibitor of the CST superfamily, involving in the processes of inflammation and tumorigenesis. This is the first exploration of the diagnostic and prognostic values of serum CST1 in NSCLC.MethodsWe analyzed the transcriptome data from The Cancer Genome Atlas and the Gene Expression Omnibus database, screened biomarkers for NSCLC, and verified the candidate markers via the ONCOMINE database. Then, we performed ELISA, western blotting, and immunohistochemistry analysis to detect the expression levels of CST1 in NSCLC cell lines, tumor tissues, and serum samples of clinical cohorts.ResultsWe identified 3 up-regulated secreted protein-encoding genes, validated the expression levels of CST1 in NSCLC tumor tissues and cell lines, and found that serum CST1 levels of NSCLC (4289 ± 2405 pg/mL) were significantly higher than those of PBN patients (1558 ± 441 pg/mL, P < .0001) and healthy controls (1529 ± 416 pg/mL, P < .0001). The AUC of the combination of CST1, Cytokeratin 19 fragment (Cyfra21-1), and Carcinoembryonic antigen (CEA) for distinguishing early-stage NSCLC from PBN/HC was as high as .914/0.925. Furthermore, our results suggested that the NSCLC patient with low serum CST1 level had a better survival rate.ConclusionsSerum CST1 may serve as a novel diagnostic marker for differentiating early-stage NSCLC from PBN and HC, and could be used as a prognosis predictor in NSCLC patients.

Microvascular Invasion is the Significant Risk Factor in Solitary Hepatocellular Carcinoma Equal or Less than 2cm

Introduction: In American Joint Committee on Cancer (AJCC) 8th edition for hepatocellular carcinoma (HCC), stage T1a was redefined as solitary tumor≤2cm irrespective of microvascular invasion(MVI). We investigated the effect of MVI on the disease free (DFS) and overall survival (OS) of small HCC≤2cm, and conducted validation of AJCC 8th edition staging. Method: From January 2000 to December 2020 at Severance Hospital, Seoul, Korea, patients who underwent liver resection and pathologically confirmed with HCC≤2cm were enrolled. The clinicopathologic characteristics, surgical and oncologic outcomes were compared by dividing into two groups according to the MVI. DFS and OS were compared between two groups after propensity score matching (PSM). We evaluated risk factors affecting DFS and OS. Results: Total 265 patients were enrolled and 22 patients of vaguely nodular type were excluded. 156 patients were MVI(-) and 87 patients were MVI(+) and 66 patients of each groups after PSM were compared. Clinicopathologic characteristics did not differ significantly except for poorly differentiated rate in major (MVI-1.5%, MVI+13.6%, p=0.011) and worst differentiation (MVI-31.8%, MVI+57.6%, p<0.001). MVI(+) group showed significantly poor DFS than MVI(-) group(p=0.007), however OS did not show statistical difference (p=0.084). Blood loss more than 150cc (HR: 1.859, p=0.026) and C-viral etiology (HR: 3.485, p=0.001) were significant risk factor for DFS in multivariate analysis. Preoperative platelet count over 150k significantly reduced death rate (HR: 0.430, p=0.049). Conclusions: Microvascular invasion is the significant risk factor for recurrence in small HCC≤2cm. AJCC 8th edition, which underestimated the prognostic effect of MVI in small HCC, should be reevaluated.

Diagnosing breast cancer tumors using stacked ensemble model

Breast cancer is the most common cancer that progresses from cells in the breast tissue among women. Early-stage detection could reduce death rates significantly, and the detection-stage determines the treatment process. Mammography is utilized to discover breast cancer at an early stage prior to any physical sign. However, mammography might return false-negative, in which case, if it is suspected that lesions might have cancer of chance greater than two percent, a biopsy is recommended. About 30 percent of biopsies result in malignancy that means the rate of unnecessary biopsies is high. So to reduce unnecessary biopsies, recently, due to its excellent capability in soft tissue imaging, Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has been utilized to detect breast cancer. Nowadays, DCE-MRI is a highly recommended method not only to identify breast cancer but also to monitor its development, and to interpret tumorous regions. However, in addition to being a time-consuming process, the accuracy depends on radiologists’ experience. Radiomic data, on the other hand, are used in medical imaging and have the potential to extract disease characteristics that can not be seen by the naked eye. Radiomics are hard-coded features and provide crucial information about the disease where it is imaged. Conversely, deep learning methods like convolutional neural networks(CNNs) learn features automatically from the dataset. Especially in medical imaging, CNNs’ performance is better than compared to hard-coded features-based methods. However, combining the power of these two types of features increases accuracy significantly, which is especially critical in medicine. Herein, a stacked ensemble of gradient boosting and deep learning models were developed to classify breast tumors using DCE-MRI images. The model makes use of radiomics acquired from pixel information in breast DCE-MRI images. Prior to train the model, radiomics had been applied to the factor analysis to refine the feature set and eliminate unuseful features. The performance metrics, as well as the comparisons to some well-known machine learning methods, state the ensemble model outperforms its counterparts. The ensembled model’s accuracy is 94.87% and its AUC value is 0.9728. The recall and precision are 1.0 and 0.9130, respectively, whereas F1-score is 0.9545.

Frequency of Hepatitis C Virus in Chronic Kidney Disease Patients on Regular Hemodialysis at Tertiary Care Hospital

Introduction: Chronic kidney disease is the most important public health issue affecting about 10% of the world adult population. Objective: To assess the frequency of Hepatitis C Virus in chronic kidney disease patients on regular hemodialysis at tertiary care hospital, Peshawar Methodology: This was descriptive cross sectional study, carried out at the Medicine department, Hayatabad Medical Complex, Peshawar for one year duration from December 2020 to December 2021. A total of 211 patients were observed in our study. All the data was statistically analyzed by using SPSS 23. Results: In our study, there were 140 (66.4%) male patients while female patients were 71(33.6%). Based on duration of illness, 149(70.6%) patients were in illness duration of 1-2 years while 62(29.4%) patients were observed in illness duration of more than 2 years (Table 1). The overall frequency of Hepatitis C virus was 68(32.2%) in patients with chronic kidney disease on regular hemodialysis Conclusion: In hospitalized CKD patients, the occurrence of hepatitis C ELISA antibody is considerably higher. To avoid hepatitis C infection spread, rigorous global infection control strategies must be applied in nephrology units. More research is required to evaluate whether detecting and treating hepatitis C in CKD patients reduces death rates or delays advancement to end-stage renal disease. Key words Hepatitis C Virus; Chronic kidney disease; Hemodialysis

The comparison of colchicine and standard treatment in COVID-19 patients: A mini review

Coronavirus disease 2019 (COVID-19) was declared a pandemic in March 2020. There are several drugs used at different stages of the disease, but none seem to be completely effective. In the absence of any known therapy, many drugs have been tested, including colchicine. Colchicine differs from these other therapies in having pleiotropic mechanism of action, being less potent on any single target, and being an oral agent. It is also a better option since colchicine is not immunosuppressive, is not known to increase risk of infection, and is inexpensive. The aim of this study was to provide evidence of the role of colchicine in improving clinical outcome and reducing death rate of COVID-19 patients through literature mini-review. A mini-review, composed of previous RCTs comparing the use of colchicine in COVID-19 with standard care or placebo under the keyword “COVID-19”, “COVID”, “coronavirus”, “SARS-CoV-2”, “coronavirus disease”, “Colchicine”, “CAS 64-86-8”, “standard treatment”, “standard care”, “standard of care”, “common treatment”, “placebo”, “clinical improvement”, and “clinical response”. The addition of colchicine to standard treatment can be beneficial in treating COVID-19. Studies included showed that colchicine has significantly improved time to clinical deterioration compared to the control group. Colchicine reduced the length of hospitalization and the need for supplemental oxygen therapy. One of the included studies mentioned that in outpatient settings, colchicine can prevent hospitalization and even death. Colchicine can improve the clinical outcome on CRP levels, the number of patients that need mechanical ventilation, and death rate in COVID-19 patients. The addition of colchicine to standard treatment should be considered to prevent clinical deterioration and death. Colchicine reduces the number of adverse effects in COVID-19 patients except for gastrointestinal adverse effects, especially diarrhea, which is consistently more common in the colchicine groups.

Association of Total Ischemic Times and In-Hospital Outcomes of Acute STEMI Patients who Underwent Primary Angioplasty at a Tertiary Cardiac Care Facility

Aims: To determine the association between total ischemic time and in-hospital outcome of acute ST elevation myocardial infarction (STEMI) patients who underwent primary angioplasty.&#x0D; Study Design: Prospective observational study.&#x0D; Place &amp; Duration of Study: Department of Cardiology, Dow university of health sciences Karachi between October 2017 till March 2021.&#x0D; Methodology: Data for total ischemic time analysis were collected from 366 STEMI patients who consecutively underwent primary angioplasty. Total ischemic time was measured from the onset of chest pain to o the first balloon inflation during primary angioplasty and in hospital outcome was measured.&#x0D; Results: Total ischemic times were available in 366 STEMI patients which was ≥ 30 minutes and &lt; 24 hours: ≤ 2 hours in 15.5%, &gt;2-3 hours in 11.4%, &gt;3-5 hours in 25.4%, and &gt;5 hours in 47.5% of STEMI patients. In addition, STEMI patients with total ischemic times &lt;5 hours demonstrated complete ST-segment resolution and reduced death rate than those with total ischemic times &gt;5 hours.&#x0D; Conclusion: This study showed that shorter ischemic times are significantly related to improved myocardial reperfusion and decreased mortality.

A quantization assisted U-Net study with ICA and deep features fusion for breast cancer identification using ultrasonic data.

Breast cancer is one of the leading causes of death in women worldwide—the rapid increase in breast cancer has brought about more accessible diagnosis resources. The ultrasonic breast cancer modality for diagnosis is relatively cost-effective and valuable. Lesion isolation in ultrasonic images is a challenging task due to its robustness and intensity similarity. Accurate detection of breast lesions using ultrasonic breast cancer images can reduce death rates. In this research, a quantization-assisted U-Net approach for segmentation of breast lesions is proposed. It contains two step for segmentation: (1) U-Net and (2) quantization. The quantization assists to U-Net-based segmentation in order to isolate exact lesion areas from sonography images. The Independent Component Analysis (ICA) method then uses the isolated lesions to extract features and are then fused with deep automatic features. Public ultrasonic-modality-based datasets such as the Breast Ultrasound Images Dataset (BUSI) and the Open Access Database of Raw Ultrasonic Signals (OASBUD) are used for evaluation comparison. The OASBUD data extracted the same features. However, classification was done after feature regularization using the lasso method. The obtained results allow us to propose a computer-aided design (CAD) system for breast cancer identification using ultrasonic modalities.

Governing a Pandemic: Assessing the Role of Collaboration on Latin American Responses to the COVID-19 Crisis

How do governments address complex, cross-sectoral problems, like the COVID-19 pandemic? Why were some Latin American countries more successful at containing the pandemic's most devastating health outcomes? We argue that national governments that were more collaborative in their response to COVID-19 were more successful in reducing death rates. Our original dataset offers a novel attempt to operationalise collaborative governance (CG). We undertake simple statistical tests to measure the relationship between CG and COVID-19-related mortality rates in Latin America. We then choose three case studies to assess whether collaboration was meaningful in practice. Initial evidence suggests governments that pursued CG were more effective at containing mortality rates early on in the pandemic. The collaboration helped to foster cooperation over resources; buy time to prepare for a potential case surge; and produce a unified message regarding what citizens should do to prevent viral spread.

Routing of pregnant women as one of the ways to reduce infant mortality

One of the most important tasks that healthcare providers face is to reduce infant mortality. The purpose of our study was to assess the effects of routing of pregnant women with preterm birth on the infant mortality rate. Materials and methods: оperating data of the Federal State Statistics Service (Rosstat), operational notifications about death of young children under the age of 1, monthly reports of obstetric hospitals subordinate to Moscow Healthcare Department, annual reports of hospital departments providing neonatal care in medical organizations of Moscow Healthcare Department, and statistical data analysis. Results and discussion. The infant mortality rate in Moscow has been declining in recent decades and has been less than 6‰ annually since 2016. The share of newborns with extremely low birth weight is insignificant in overall rate. At the same time, extremely low birth weight is one of the main causes of infant mortality. As this category of newborns requires specific care, it is better to deliver such babies in perinatal centers that provide high quality medical care. Within 5 years, the share of children with ELBW born in perinatal centers and specialized obstetric hospitals has almost doubled. At the same time, there is an increase in the survival rate of children with extremely low birth weight by the age of one year in obstetric hospitals. Conclusion. Well-planned routing of patient within the large system of obstetric hospitals in Moscow could reduce death rate, increase the survival rate of children with extremely low birth weight and contribute to reducing infant mortality in the region.

The Effects of Chinese Medicine QRD, Antibiotics, and Probiotics on Therapy and Gut Microbiota in Septic Rats.

Sepsis is a common and often treacherous medical emergency with a high mortality and long-term complications in survivors. Though antibiotic therapy can reduce death rate of sepsis significantly, it impairs gut microbiota (GM), which play imperative roles in human health. In this study, we compared the therapeutic effects of antibiotics, probiotics, and Chinese medicine QRD on the survival rates of septic model and observed the GM characteristics of experimental rats via 16S rRNA gene amplicon sequencing. The 72 h survival rates of septic rat demonstrated the significant therapeutic effects in the three groups treated with antibiotics (AT), Chinses medicine QRD (QT), and probiotics (PT), which were elevated from the survival rate of 26.67% for the sepsis control group (ST) to 100.0% for AT, 88.24% for QT, and 58.33% for PT. The original characteristics of GM identified in the sham operation controls (SC) were relatively similar to those in PT and QT; nevertheless, the AT rats were shown dramatically decreased in the GM diversity. In addition, the septic rats in AT were revealed the higher abundances of Escherichia Shigella, Proteus, Morganella, Enterococcus, and Lysinibacillus, but the lower those of Parabacteroides, Alistipes, Desulfovibrio, Bacteroides, Helicobacter, Mucispirillum, Oscillibacter, Lachnospiraceae, and Ruminiclostridium 9, when compared to the PT and QT rats. By contrast, the GM of PT and QT rats shared similar diversity and structure. Our findings indicated that QRD increased the survival rates without impairment of the GM characteristics, which provides novel insights into the role of Chinese medicine in therapy and long-term recovery of sepsis.

Early Decompressive Craniectomy: A Life Saving Option in Closed Head Trauma

Aim: To determine outcome and factors associated with outcome among patients with closed head injury who underwent decompressive craniectomy. Methodology: Cross sectional analytical study conducted in Neurosurgery Department of teaching hospital Dera Ghazi Khan from January, 2021 to June, 2021. Total 105 patients fulfilling the inclusion criteria were enrolled in the study. Approval of ethical review committee was obtained. All the patients with closed head injury which underwent decompressive craniectomy were included. Socio demographic profile, mode of injury, Glasgow Coma Scale (GCS) at admission time, pupillary reaction and timing from hospital admission to surgery, duration of surgery, length of hospital stay and occurrence of CSF leakage was noted. SPSS version 22 was used for data entry and analysis. Results: Head injury was more common in males. 43.8% cases admitted with GCS score between 3-8 and with head injury by road traffic accident. Leakage of CSF was recorded in 13.3% patients. Mortality was recorded in 18.1% patients. GCS at time of admission, time elapsed between admission and surgery and duration of surgery was significantly associated with the outcome. Conclusion: Early decompressive craniectomy significantly reduce death rate in patients with closed head injury. Keywords: Craniectomy, Outcome, Glasgow coma scale

A CNN-LSTM network with multi-level feature extraction-based approach for automated detection of coronavirus from CT scan and X-ray images

Auto-detection of diseases has become a prime issue in medical sciences as population density is fast growing. An intelligent framework for disease detection helps physicians identify illnesses, give reliable and consistent results, and reduce death rates. Coronavirus (Covid-19) has recently been one of the most severe and acute diseases in the world. An automatic detection framework should therefore be introduced as the fastest diagnostic alternative to avoid Covid-19 spread. In this paper, an automatic Covid-19 identification in the CT scan and chest X-ray is obtained with the help of a combined deep learning and multi-level feature extraction methodology. In this method, the multi-level feature extraction approach comprises GIST, Scale Invariant Feature Transform (SIFT), and Convolutional Neural Network (CNN) extract features from CT scans and chest X-rays. The objective of multi-level feature extraction is to reduce the training complexity of CNN network, which significantly assists in accurate and robust Covid-19 identification. Finally, Long Short-Term Memory (LSTM) along the CNN network is used to detect the extracted Covid-19 features. The Kaggle SARS-CoV-2 CT scan dataset and the Italian SIRM Covid-19 CT scan and chest X-ray dataset were employed for testing purposes. Experimental outcomes show that proposed approach obtained 98.94% accuracy with the SARS-CoV-2 CT scan dataset and 83.03% accuracy with the SIRM Covid-19 CT scan and chest X-ray dataset. The proposed approach helps radiologists and practitioners to detect and treat Covid-19 cases effectively over the pandemic.

Investigation of Some Immunological Markers in Covid-19 Vaccinated Individuals: A Review Article

The COVID 19 pandemic has caused great havoc amongst the people in society across the globe. It has affected people both physically and emotionally. The epicentre of the virus has been attributed to Wuhan, from where it spread in the different parts of the globe. Vaccination emerged as one of the best solutions to deal with the proliferation of the virus. It also helped people to protect themselves against hospitalization and reduced death rates. The researcher has used Secondary sources to gather information and data related to the immunological markers in Covid-19 vaccinated population. A major immune marker is the manufacturing of neutralizing antibodies. However, the further implication in this aspect is still under research by scientists.

Towards for Designing Intelligent Health Care System Based on Machine Learning

Health Information Technology (HIT) provides many opportunities for transforming and improving health care systems. HIT enhances the quality of health care delivery, reduces medical errors, increases patient safety, facilitates care coordination, monitors the updated data over time, improves clinical outcomes, and strengthens the interaction between patients and health care providers. Living in modern large cities has a significant negative impact on people’s health, for instance, the increased risk of chronic diseases such as diabetes. According to the rising morbidity in the last decade, the number of patients with diabetes worldwide will exceed 642 million in 2040, meaning that one in every ten adults will be affected. All the previous research on diabetes mellitus indicates that early diagnoses can reduce death rates and overcome many problems. In this regard, machine learning (ML) techniques show promising results in using medical data to predict diabetes at an early stage to save people’s lives. In this paper, we propose an intelligent health care system based on ML methods as a real-time monitoring system to detect diabetes mellitus and examine other health issues such as food and drug allergies of patients. The proposed system uses five machine learning methods: K-Nearest Neighbors, Naïve Bayes, Logistic Regression, Random Forest, and Support Vector Machine (SVM). The system selects the best classification method with high accuracy to optimize the diagnosis of patients with diabetes. The experimental results show that in the proposed system, the SVM classifier has the highest accuracy of 83%.