Survival rate in hypertensive patients with COVID-19.

INTRODUCTION: A life-threatening respiratory disease, coronavirus 2019 (COVID-19), has spread across the globe since December 2019. Many prognostic factors have already been put forward to predict the risk of death and other outcomes. The current study is evaluating the survival rate between hypertensive and non-hypertensive infected patients. METHODS: Patients who were included in this study were admitted between 20 February to 1 March 2020 in Fars (southwest of Iran) province hospitals. Data were collected from the electronic base registry which contained demographic information, medical symptoms, and signs, underlying diseases, CT scan results, and final outcome. RESULTS: Of all 1239 positive cases, 159 (12.83%) had known with hypertension ant this group was significantly older than non-hypertensive patients (66.1 years Vs 48.95 years, p < .001). According to Kaplan-Meier survival curve and log-rank test, it was seen hypertensive patients deteriorated more rapidly than non-hypertensive group (p = .032). Moreover, HIV, cardiovascular, and kidney disease were diagnosed as factors that increase the risk of death in hypertensive patients. CONCLUSION: The current study about the survival rate of COVID-19 patients had shown hypertensive patents are in danger of disease severity, although it may be related to their age. Moreover, the probability of other complications like diabetes, smoking, asthma, kidney, and cardiovascular diseases, and either some other infections such as HIV can make the condition complicated and need more consideration to prevent noxious outcomes.

Seizure in patients with COVID-19.

OBJECTIVE: The purpose of the current study was to collect the data on the occurrence of seizures in patients with COVID-19 and to clarify the circumstances of the occurrence of seizures in these patients. METHODS: All consecutive patients who referred to healthcare facilities anywhere in Fars province (located in South Iran with a population of 4.851 million people) from February 19 until June 2, 2020, and had confirmed COVID-19 by positive result on polymerase chain reaction testing and seizure were included. RESULTS: During the study period, 6,147 people had confirmed COVID-19 in Fars province, Iran; 110 people died from the illness (case fatality rate 1.79%). During this time period, five people had seizures (seizure rate 0.08%). In four patients, seizure was one of the presenting manifestations, and in one person, it happened during the course of hospital admission. Two patients had status epilepticus. All patients experienced hypoxemia and four of them needed respirator. Two patients had related metabolic derangements and one had cerebrospinal fluid (CSF) lymphocytic pleocytosis. Brain imaging was abnormal in three patients. Four patients died. CONCLUSION: New-onset seizures in critically ill patients with COVID-19 should be considered as acute symptomatic seizures and the treating physician should try to determine the etiology of the seizure and manage the cause immediately and appropriately. Detailed clinical, neurological, imaging, and electrophysiological investigations and attempts to isolate SARS-CoV-2 from CSF may clarify the role played by this virus in causing seizures in these patients.

Death Reporting in Breakthrough and Unvaccinated SARS-CoV-2 Infection Cases.

Vaccines are undeniably an important tool for controlling infectious disease outbreaks, and they are the most certain way to end the epidemic risk. This brief report describes the characteristics of coronavirus disease 2019 (COVID-19) deaths among breakthrough and unvaccinated cases hospitalized in Fars province in the south of Iran. This cross-sectional study was performed to compare breakthrough and unvaccinated death cases in Fars, Iran (February 2, to August 19, 2021). Among 444,728 fully vaccinated people, 60,800 breakthrough cases were detected. Thus, 501 died, of which 297 (297/501) cases were hospitalized and compared with the unvaccinated dead group. The median age for breakthrough and unvaccinated cases was estimated 79 and 65 y, respectively. All signs and symptoms of COVID-19 were more frequent in the unvaccinated group. Decreasing O2 saturation (less than 93%) happened more often in the unvaccinated group significantly. Unvaccinated dead patients had significantly shorter hospital stays. These patients received 66.63% Sinopharm, 0.67% Sputnik, 0.67% COVIran Barekat, and 31.99% AstraZeneca vaccines. None of them were health-care staff. Equitable access to safe and effective vaccines is critical to ending the COVID-19 pandemic. As vaccine uptake increases, we observed a decrease in mortality and protection from severe forms of the disease.

Liver Biomarkers Assay in COVID-19 Cases: A Comparison Study between Alive and Dead Patients.

BACKGROUND: Identifying effective biomarkers plays a critical role on screening; rapid diagnosis; proper managements and therapeutic options, which is helpful in preventing serious complications. The present study aimed to compare the liver laboratory tests between alive and dead hospitalized cases for prediction and proper management of the patients. METHODS: This retrospective, cross sectional study consists of all deceased patients admitted in one center in Shiraz, Iran during 19 Feb 2020 to 22 Aug 2021. For further comparison, we selected a 1:2 ratios alive group randomly. RESULTS: Overall, 875 hospitalized cases died due to COVID-19. We selected 1750 alive group randomly. The median age was significantly higher in died group (65.96 vs 51.20). Regarding the laboratory findings during the hospitalization ALT, AST, Bili.D were significantly higher in non-survivors than survivors but Albumin was less in deceased patients. It was revealed elevated levels of Albumin, AST, Bili.T and Bili.D were associated with increasing the risk of in hospital death. Moreover, the predictive effect of ALP and Bili.D had significantly more than others with high sensitivity and specify. CONCLUSION: We found patients with COVID-19 have reduced serum albumin level, and increase ALT and AST. The current results revealed abnormal liver chemistries is associated with poor outcome, which highlighted the importance of monitoring these patients more carefully and should be given more caution.

A universal methodology for reliable predicting the non-steroidal anti-inflammatory drug solubility in supercritical carbon dioxide.

Understanding the drug solubility behavior is likely the first essential requirement for designing the supercritical technology for pharmaceutical processing. Therefore, this study utilizes different machine learning scenarios to simulate the solubility of twelve non-steroidal anti-inflammatory drugs (NSAIDs) in the supercritical carbon dioxide (SCCO2). The considered NSAIDs are Fenoprofen, Flurbiprofen, Ibuprofen, Ketoprofen, Loxoprofen, Nabumetone, Naproxen, Nimesulide, Phenylbutazone, Piroxicam, Salicylamide, and Tolmetin. Physical characteristics of the drugs (molecular weight and melting temperature), operating conditions (pressure and temperature), and solvent property (SCCO2 density) are effectively used to estimate the drug solubility. Monitoring and comparing the prediction accuracy of twelve intelligent paradigms from three categories (artificial neural networks, support vector regression, and hybrid neuro-fuzzy) approves that adaptive neuro-fuzzy inference is the best tool for the considered task. The hybrid optimization strategy adjusts the cluster radius of the subtractive clustering membership function to 0.6111. This model estimates 254 laboratory-measured solubility data with the AAPRE = 3.13%, MSE = 2.58 × 10-9, and R2 = 0.99919. The leverage technique confirms that outliers may poison less than four percent of the experimental data. In addition, the proposed hybrid paradigm is more reliable than the equations of state and available correlations in the literature. Experimental measurements, model predictions, and relevancy analyses justified that the drug solubility in SCCO2 increases by increasing temperature and pressure. The results show that Ibuprofen and Naproxen are the most soluble and insoluble drugs in SCCO2, respectively.

Constructing an Intelligent Model Based on Support Vector Regression to Simulate the Solubility of Drugs in Polymeric Media.

This study constructs a machine learning method to simultaneously analyze the thermodynamic behavior of many polymer-drug systems. The solubility temperature of Acetaminophen, Celecoxib, Chloramphenicol, D-Mannitol, Felodipine, Ibuprofen, Ibuprofen Sodium, Indomethacin, Itraconazole, Naproxen, Nifedipine, Paracetamol, Sulfadiazine, Sulfadimidine, Sulfamerazine, and Sulfathiazole in 1,3-bis[2-pyrrolidone-1-yl] butane, Polyvinyl Acetate, Polyvinylpyrrolidone (PVP), PVP K12, PVP K15, PVP K17, PVP K25, PVP/VA, PVP/VA 335, PVP/VA 535, PVP/VA 635, PVP/VA 735, Soluplus analyzes from a modeling perspective. The least-squares support vector regression (LS-SVR) designs to approximate the solubility temperature of drugs in polymers from polymer and drug types and drug loading in polymers. The structure of this machine learning model is well-tuned by conducting trial and error on the kernel type (i.e., Gaussian, polynomial, and linear) and methods used for adjusting the LS-SVR coefficients (i.e., leave-one-out and 10-fold cross-validation scenarios). Results of the sensitivity analysis showed that the Gaussian kernel and 10-fold cross-validation is the best candidate for developing an LS-SVR for the given task. The built model yielded results consistent with 278 experimental samples reported in the literature. Indeed, the mean absolute relative deviation percent of 8.35 and 7.25 is achieved in the training and testing stages, respectively. The performance on the largest available dataset confirms its applicability. Such a reliable tool is essential for monitoring polymer-drug systems' stability and deliverability, especially for poorly soluble drugs in polymers, which can be further validated by adopting it to an actual implementation in the future.

Folic Acid-Decorated pH-Responsive Nanoniosomes With Enhanced Endocytosis for Breast Cancer Therapy: In Vitro Studies.

Breast cancer is the most common invasive cancer in women and the second leading cause of cancer death in women after lung cancer. The purpose of this study is a targeted delivery toward in vitro (on MCF7 and 4T1 breast cancer cell lines) through niosomes-based nanocarriers. To this end, different bioactive molecules, including hyaluronic acid (HA), folic acid (FA), and polyethylene glycol (PEG), were used and compared for surface modification of niosomes to enhance endocytosis. FA-functionalized niosomes (Nio/5-FU/FA) were able to increase cell cytotoxicity and reduce cell migration and invasion compared to PEG-functionalized niosomes (Nio/5-FU/PEG), and HA-functionalized niosomes (Nio/5-FU/HA) groups in MCF-7 and 4T1 cell lines. Although the Nio/5-FU/PEG and Nio/5-FU/HA demonstrated MCF7 cell uptake, the Nio/5-FU/FA exhibited the most preponderant endocytosis in pH 5.4. Remarkably, in this study 5-FU loaded niosomes (nonionic surfactant-based vesicles) were decorated with various bioactive molecules (FA, PEG, or HA) to compare their ability for breast cancer therapy. The fabricated nanoformulations were readily taken up by breast cancer cells (in vitro) and demonstrated sustained drug release characteristics, inducing cell apoptosis. Overall, the comprehensive comparison between different bioactive molecules-decorated nanoniosomes exhibited promising results in finding the best nano formulated candidates for targeted delivery of drugs for breast cancer therapy.

Lung Cancer Diagnosis Based on an ANN Optimized by Improved TEO Algorithm.

A quarter of all cancer deaths are due to lung cancer. Studies show that early diagnosis and treatment of this disease are the most effective way to increase patient life expectancy. In this paper, automatic and optimized computer-aided detection is proposed for lung cancer. The method first applies a preprocessing step for normalizing and denoising the input images. Afterward, Kapur entropy maximization is performed along with mathematical morphology to lung area segmentation. Afterward, 19 GLCM features are extracted from the segmented images for the final evaluations. The higher priority images are then selected for decreasing the system complexity. The feature selection is based on a new optimization design, called Improved Thermal Exchange Optimization (ITEO), which is designed to improve the accuracy and convergence abilities. The images are finally classified into healthy or cancerous cases based on an optimized artificial neural network by ITEO. Simulation is compared with some well-known approaches and the results showed the superiority of the suggested method. The results showed that the proposed method with 92.27% accuracy provides the highest value among the compared methods.

A New Optimal Diagnosis System for Coronavirus (COVID-19) Diagnosis Based on Archimedes Optimization Algorithm on Chest X-Ray Images.

The new coronavirus, COVID-19, has affected people all over the world. Coronaviruses are a large group of viruses that can infect animals and humans and cause respiratory distress; these discomforts may be as mild as a cold or as severe as pneumonia. Correct detection of this disease can help to avoid its spreading increasingly. In this paper, a new CAD-based approach is suggested for the optimal diagnosis of this disease from chest X-ray images. The proposed method starts with a min-max normalization to scale all data into a normal scale, and then, histogram equalization is performed to improve the quality of the image before main processing. Afterward, 18 different features are extracted from the image. To decrease the method difficulty, the minimum features are selected based on a metaheuristic called Archimedes optimization algorithm (AOA). The model is then implemented on three datasets, and its results are compared with four other state-of-the-art methods. The final results indicated that the proposed method with 86% accuracy and 96% precision has the highest balance between accuracy and reliability with the compared methods as a diagnostic system for COVID-19.

Characteristics of deceased patients with CoVID-19 after the first peak of the epidemic in Fars province, Iran.

Emergence of a new coronavirus causes a serious concern whether this can be stopped at all. The ongoing coronavirus disease created a substantial variation in the fatality rate over the world. The current report brought an explore about the epidemiological characteristics of deceased patients and the fatality rate after the first peak in Fars province which is the fourth most populous and large province in Iran. Of the 3702 confirmed cases with coVID-19, 87 patients passed away and so the fatality rate estimated 2.35. Also, it was derived that male sex, old age and underlying diseases especially diabetes were common characteristics of these victims.