Evaluation of blood pressure variation in recovered COVID-19 patients at one-year follow-up: a retrospective cohort study.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has various sequelae, one of which might be hypertension. We aimed to evaluate COVID-19's impact on blood pressure (BP) in non-hospitalized patients at one-year follow-up. METHOD: A total of 7,950 consecutive COVID-19 patients regularly visiting our cardiology clinic were retrospectively screened. Patients' electronic medical records including demographics, comorbidities, vital signs, treatments, and outcomes, were reviewed by two physicians. Individuals with at least one BP measurement in the three months preceding COVID-19 and one measurement in 12 months or more following recovery were included. BP levels before and after COVID-19 were compared using the paired t-test. RESULTS: 5,355 confirmed COVID-19 patients (mean age 55.51 ± 15.38 years) were included. Hypertension (56.9%) and diabetes mellitus (34%) were the predominant comorbidities, and 44.3% had prior major adverse cardiovascular events. Both systolic (126.90 ± 20.91 vs. 139.99 ± 23.94 mmHg, P < 0.001) and diastolic BP (80.54 ± 13.94 vs. 86.49 ± 14.40 mmHg, P < 0.001) were significantly higher post-COVID-19 vs. pre-COVID-19. Notably, 456 (14%) hypertensive patients experienced exacerbated hypertension, while 408 (17%) patients developed new-onset hypertension, overall 864 (16%) of patients had exacerbation or new hypertension. Linear regression analysis revealed that advanced age, smoking, previous cardiovascular events, hypertension, and diabetes mellitus predict increased BP following COVID-19 (P < 0.001). CONCLUSION: COVID-19 raised systolic and diastolic BP in the long term in non-hospitalized patients, with over one-sixth developing new-onset or exacerbated hypertension. All patients should be evaluated regarding BP, following COVID-19 recovery, particularly those with the mentioned predictive factors. (clinicaltrial.gov: NCT05798208).

Endothelial cells' biophysical, biochemical, and chromosomal aberrancies in high-glucose condition within the diabetic range.

To date, many studies have been conducted to find out the underlying mechanisms of hyperglycemia-induced complications in diabetes mellitus, attributed to the cellular pathologies of different cells-especially endothelial cells. However, there are still many ambiguities and unresolved issues to be clarified. Here, we investigated the alteration in biophysical and biochemical properties in human umbilical vein endothelial cells exposed to a high-glucose concentration (30mM), comparable to glucose content in type 2 diabetes mellitus, over a course of 120 hours. In addition to a reduction in the rate of cell viability and induction of oxidative stress orchestrated by the high-glucose condition, the dynamic of the fatty acid profile-including polyunsaturated, monounsaturated, and saturated fatty acids-was also altered in favor of saturated fatty acids. Genetic imbalances were also detected at chromosomal level in the cells exposed to the abnormal concentration of glucose after 120 hours. Moreover, the number of tip cells (CD31+ /CD34+ ) and in vitro tubulogenesis capability negatively diminished in comparison to parallel control groups. We found that diabetic hyperglycemia was associated with a decrease in the cell-cell tight junction and upregulation in vascular endothelial cadherin and zonula occludens (ZO)-1 molecules after 72 and 120 hours of exposure to the abnormal glucose concentration, which resulted in a profound reduction in transendothelial electrical resistance. The surface plasmon resonance analysis of the human umbilical vein endothelial cells immobilized on gold-coated sensor chips confirmed the loosening of the cell to cell intercellular junction as well as stable attachment of each cell to the basal surface. Our findings highlighted the disturbing effects of a diabetic hyperglycemia on either biochemical or biophysical properties of endothelial cells.

Mass detection in digital breast tomosynthesis data using convolutional neural networks and multiple instance learning.

Digital breast tomosynthesis (DBT) was developed in the field of breast cancer screening as a new tomographic technique to minimize the limitations of conventional digital mammography breast screening methods. A computer-aided detection (CAD) framework for mass detection in DBT has been developed and is described in this paper. The proposed framework operates on a set of two-dimensional (2D) slices. With plane-to-plane analysis on corresponding 2D slices from each DBT, it automatically learns complex patterns of 2D slices through a deep convolutional neural network (DCNN). It then applies multiple instance learning (MIL) with a randomized trees approach to classify DBT images based on extracted information from 2D slices. This CAD framework was developed and evaluated using 5040 2D image slices derived from 87 DBT volumes. The empirical results demonstrate that this proposed CAD framework achieves much better performance than CAD systems that use hand-crafted features and deep cardinality-restricted Bolzmann machines to detect masses in DBTs.

New Product Development in the Pharmaceutical Industry: Evidence from a generic market.

In today's competitive world, there are several strategies to deal with the fast changing environment, among which New product development (NPD) is a common strategy. However, almost half of the resources that companies devote to NPD are spent on products that may fail. This issue is particularly highlighted in the pharmaceutical industry mainly because of a long development-time, low success rate, high capital requirement, and market uncertainty. This study identifies critical success factors of NPD based on the relevant literatures and expert opinions in Iranian pharmaceutical industry, then prioritizes them using the methodology of multiple criteria decision making (MCDM) through analyzing 50 filled questionnaires structured based on the AHP (Analytical Hierarchy Process) approach. Although the NPD success factors seem the same in both generic and bio-generic pharmaceutical industries, the underlying factors and related sub-factors show the different importance in these two industries. However, this study reveal that, the company capabilities is the most important factor affecting new product development success in both pharmaceutical generic and bio-generic industry. The results of this study contribute to create baseline information for pharmaceutical industry especially Iranian pharmaceutical companies to be more effective in budget allocation on improving NPD success factors so that they can boost the success rate of NPD more effectively.