Search

This study aimed to assess the severity and outcomes of COVID-19 in pregnant women, focusing on laboratory and radiological discrepancies between pregnant women and matched nonpregnant women. In this retrospective cross-sectional analysis, we matched 107 nonpregnant women with 66 pregnant women in terms of age, comorbidities, and the interval between symptom onset and hospital admission. Demographic, clinical, laboratory, and radiological data were collected, and chest CT scans were evaluated using a severity scale ranging from 0 to 5. Logistic regression and adjusted Cox regression models were used to assess the impact of various factors on pregnancy status and mortality rates. Differences in several laboratory parameters, including the neutrophil-to-lymphocyte ratio, liver aminotransferases, alkaline phosphatase, urea, triglycerides, cholesterol, HbA1c, ferritin, coagulation profiles, and blood gases, were detected. Radiologic exams revealed that nonpregnant women had sharper opacities, whereas pregnant women presented with hazy opacities and signs of crypt-organizing pneumonia. A notable difference was also observed in the pulmonary artery diameter. The mortality rate among pregnant women was 4.62%, which was comparable to the 5.61% reported in nonpregnant patients. Compared with nonpregnant patients, pregnancy did not significantly affect the severity or mortality of COVID-19. Our study revealed discernible differences in specific laboratory and imaging markers between pregnant and nonpregnant COVID-19 patients. [ABSTRACT FROM AUTHOR]

This study investigates the risk of chronic kidney disease (CKD) across four metabolic phenotypes: Metabolically Healthy-No Obesity (MH-NO), Metabolically Unhealthy-No obesity (MU-NO), Metabolically Healthy-Obesity (MH-O), and Metabolically Unhealthy-Obesity (MU-O). Data from the Tehran Lipid and Glucose Study, collected from 1999 to 2020, were used to categorize participants based on a BMI ≥ 30 kg/m2 and metabolic health status, defined by the presence of three or four of the following components: high blood pressure, elevated triglycerides, low high-density lipoprotein, and high fasting blood sugar. CKD, characterized by a glomerular filtration rate < 60 ml/min/1.72 m2. The hazard ratio (HR) of CKD risk was evaluated using Cox proportional hazard models. The study included 8731 participants, with an average age of 39.93 years, and identified 734 incidents of CKD. After adjusting for covariates, the MU-O group demonstrated the highest risk of CKD progression (HR 1.42–1.87), followed by the MU-NO group (HR 1.33–1.67), and the MH-O group (HR 1.18–1.54). Persistent MU-NO and MU-O posed the highest CKD risk compared to transitional states, highlighting the significance of exposure during early adulthood. These findings emphasize the independent contributions of excess weight and metabolic health, along with its components, to CKD risk. Therefore, preventive strategies should prioritize interventions during early-adulthood. [ABSTRACT FROM AUTHOR]

This large-scale study aimed to investigate the trend of laboratory tests of patients with COVID-19. Hospitalized confirmed and probable COVID-19 patients in three general hospitals were examined from March 20, 2020, to June 18, 2021. The confirmed and probable COVID-19 patients with known outcomes and valid laboratory results were included. The least absolute shrinkage and selection operator (LASSO) and Cox regression were used to select admittance prognostic features. Parallel Pairwise Comparison of mortality versus survival was used to examine the trend of markers. In the final cohort, 11,944 patients were enrolled, with an in-hospital mortality rate of 21.8%, mean age of 59.4 ± 18.0, and a male-to-female ratio of 1.3. Abnormal admittance level of white blood cells, neutrophils, lymphocytes, mean cellular volume, urea, creatinine, bilirubin, creatine kinase-myoglobin binding, lactate dehydrogenase (LDH), Troponin, c-reactive protein (CRP), potassium, and creatinine phosphokinase reduced the survival of COVID-19 inpatients. Moreover, the trend analysis showed lymphocytes, platelet, urea, CRP, alanine transaminase (ALT), and LDH have a dissimilar trend in non-survivors compared to survived patients. This study proposed a novel approach to find serial laboratory markers. Serial examination of platelet count, creatinine, CRP, LDH, and ALT can guide healthcare professionals in finding patients at risk of deterioration. [ABSTRACT FROM AUTHOR]

We aimed to propose a mortality risk prediction model using on-admission clinical and laboratory predictors. We used a dataset of confirmed COVID-19 patients admitted to three general hospitals in Tehran. Clinical and laboratory values were gathered on admission. Six different machine learning models and two feature selection methods were used to assess the risk of in-hospital mortality. The proposed model was selected using the area under the receiver operator curve (AUC). Furthermore, a dataset from an additional hospital was used for external validation. 5320 hospitalized COVID-19 patients were enrolled in the study, with a mortality rate of 17.24% (N = 917). Among 82 features, ten laboratories and 27 clinical features were selected by LASSO. All methods showed acceptable performance (AUC > 80%), except for K-nearest neighbor. Our proposed deep neural network on features selected by LASSO showed AUC scores of 83.4% and 82.8% in internal and external validation, respectively. Furthermore, our imputer worked efficiently when two out of ten laboratory parameters were missing (AUC = 81.8%). We worked intimately with healthcare professionals to provide a tool that can solve real-world needs. Our model confirmed the potential of machine learning methods for use in clinical practice as a decision-support system. [ABSTRACT FROM AUTHOR]

The present study aimed at evaluating the prevalence of general and sinonasal symptoms in patients with olfactory symptoms and mild coronavirus disease-2019 (COVID-19) and determining the patterns in emergence and resolution of olfactory/gustatory symptoms relative to general and sinonassal symptoms. This was a prospective cross-sectional study conducted at the outpatient otorhinolaryngology clinic at a COVID-19-designated referral Hospital. We included consecutive patients with new-onset olfactory dysfunction and positive polymerase chain reaction (PCR) assay of COVID-19. We asked the patients to fill in a questionnaire about general and sinonasal symptoms in association with anosmia, hyposmia or hypogeusia, and recorded the time course of the olfactory/gustatory symptoms during 2-weeks of follow-up. 76 patients with average age of 38.5 ± 10.6 years were included. Majority of participants (94.7%) had general or sinonasal symptom. There was anosmia in 60.5% and hyposmia in 39.5%, with sudden onset of olfactory symptoms reported in 63.2% of patients. During the follow-up, 30.3% of patients completely and 44.7% partially recovered from anosmia/hyposmia. Regardless of whether the general or olfactory symptoms appeared initially, the general symptoms resolved first while a degree of olfactory dysfunction persisted during the follow-up. Our study showed that hyposmia and anosmia in mild COVID-19 are frequently associated with general and sinonasal symptoms and tend to persist longer than the general and sinonasal symptoms during the course of the disease. [ABSTRACT FROM AUTHOR]

Outsourcing computation has gained significant attention in recent years in particular due to the prevalence of cloud computing. There are two main security concerns in outsourcing computation: guaranteeing that the server performs the computation correctly, and protecting the privacy of the client's data. The verifiable computation of Gennaro, Gentry and Parno addresses both concerns for outsourcing the computation of a function f on an input x to the cloud. The GGP scheme is privately delegatable, privately verifiable, and based on the expensive cryptographic primitives such as fully homomorphic encryption (FHE). In this paper we consider the problem of outsourcing matrix-vector multiplications of the form F x , where F is a matrix and x is a column vector, and construct publicly delegatable and publicly verifiable schemes. Our schemes are either input private or function private, highly efficient, and provably secure under the well-established assumptions such as the discrete-logarithm assumption. We decompose a polynomial computation, such as computing a univariate polynomial of arbitrary degree, a bivariate polynomial of arbitrary degree, a quadratic multivariate polynomial, and in general any multivariate polynomial, into a two-step computation in which the computaionally expensive step is a matrix-vector multiplication. We use the matrix schemes to outsource the computation of high-degree polynomials and obtain the first high-degree polynomial outsourcing schemes that simultaneously have public delegation, public verification and input privacy/function privacy. [ABSTRACT FROM AUTHOR]

Sharing resources is a growing trend in today's world. Sharing allows larger groups of individuals to benefit from available resources, hence optimizing the resource consumption. Sharing however demands a level of trust and control on the outcome: one needs to have confidence that a shared item will be used according to a prior agreement. In this paper we rely on geographic proximity of users to provide as an initial level of trust, and use this trust to develop a platform for sharing digital goods in a geographic neighbourhood. We require the platform to allow users to express their preferences and be confident about the sharing outcome. Our proposed platform uses a permissioned blockchain that is maintained by network providers whose subscribers are in the same geographic area, and use attribute-based access control to specify and control accesses to the shared items. Our proposed system uses smart contracts to enforce the conditions of access. We analyze security of the proposed design and discuss directions for future work. [ABSTRACT FROM AUTHOR]

There are limited numbers of articles, studying combined use of antihistamines. In this study, we compare single therapy of Apo-Cetirizine with a new regimen of intermittent sequential therapy with cetirizine, loratadine and chlorpheniramine in treatment of seasonal allergic rhinitis. This randomized clinical trial was performed between April and September at the peak prevalence of seasonal allergic rhinitis. Fifty-four eligible patients diagnosed clinically to have seasonal allergic rhinitis were randomized in two groups: 24 cases in single therapy arm, received Apo-Cetirizine 10 mg tablet daily and in other arm, 30 patients received sequential regimen of cetirizine 10 mg tablet, loratadine 10 mg tablet and chlorpheniramine 4 mg tablet, one tablet each day. Major Symptom Complex Score (MSCS) and Total Symptom Complex Score (TSCS) of patients were recorded before treatment and after 30 days of treatment in two groups. The average post-treatment MSCS and TSCS in combination therapy group showed better improvement than single therapy group but difference was not statistically significant ( p value = 0.053 and p value = 0.104 respectively). Combination therapy regimen was better in improvement of nasal congestion ( p value = 0.006). There were no significant difference between two groups in efficacy, side effects and patient's satisfaction. Combination therapy would be effective on a wide spectrum of symptoms with lower price and theoretically offers lower chance of tolerance and re-appearance of complaints. [ABSTRACT FROM AUTHOR]

We present a Hoare logic for proving semantic security and determining exact security bounds of a block cipher mode of operation. We propose a simple yet expressive programming language to specify encryption modes, semantic functions for each command (statement) in the language, an assertion language that allows to state predicates and axioms, and rules to propagate the predicates through the commands of a program. We also provide heuristics for finding loop invariants that are necessary for the application of our rule on for-loops. This enables us to prove the security of protocols that take arbitrary length messages as input. We implemented a prototype that uses this logic to automatically prove the security of block cipher modes of operation. This prototype can prove the security of many standard modes of operation, such as Cipher Block Chaining (CBC), Cipher FeedBack mode (CFB), Output FeedBack (OFB), and CounTeR mode (CTR). [ABSTRACT FROM AUTHOR]

Verifiable delegation of computation has attracted considerable attention in recent years and had resulted in a number of delegation models. In the verifiable computation model of Gennaro et al., a client wants to delegate the computation of a function to a cloud server. The client generates an encoding of the function that will be stored on the cloud server, and has the property that the cloud server can compute the function on any client's supplied input together with a correctness proof. The proof will allow the client to verify the correctness of the computation by the server using substantially less time than performing the computation itself. In all existing verifiable computation schemes the encoding of the delegated function requires at least twice as much cloud storage as the function itself. This stringent requirement on storage can become a bottleneck in practice. In this paper, we introduce batch verifiable computation which enables the simultaneous delegation of multiple functions. We construct batch verifiable computation schemes that effectively reduce the requirement on cloud storage while preserving efficient client verification. To delegate $$s$$ functions, our batch verifiable computation schemes only require $$1+1/s$$ as much cloud storage as the $$s$$ functions themselves. Our schemes are asymptotically optimal in terms of cloud storage in the sense that $$1+1/s\rightarrow 1$$ as $$s\rightarrow \infty $$ . We start with an information-theoretic construction and then introduce computational assumptions to obtain the above efficiencies. [ABSTRACT FROM AUTHOR]