Lumbar artery pseudoaneurysm following percutaneous nephrolithotripsy: Treatment by transcatheter embolization

Vascular complications from percutaneous nephrostomy/nephrolithotripsy (PCN/PCNL) mostly involve the kidneys. Lumbar artery pseudoaneurysms from PCN and PCNL are a rare occurrence. We report a case of lumbar artery pseudoaneurysm following PCNL. This was treated successfully by transcatheter embolization

Antibacterial Nanomaterials for Dental Implants: From Coatings to Surface Modifications

Purpose: Nanomaterials have a plethora of applications in the field of dental sciences. Due to their peculiar properties and distinguishing characteristics, nanomaterials are used as candidates for coating titanium, acrylic, and PLA/PCL-based dental implants. This article reviews various nanomaterials that can impart antibacterial properties and be used as implant coating materials. Methods: Research articles published between 1992 and 2021, based on nanomaterial coatings for dental applications, were retrieved from Google Scholar and were thoroughly studied. Results: The research articles reviewed indicated that a substantial amount of progress had been made in the area of antibacterial resistance of nanomaterial-based coatings for dental implants. This work’s main contribution lies in a comprehensive review of the various nanostructures that inhibit the nurturing of bacterial biofilms on implants, thus diminishing the probability of any infection. Significance: Given the reported results, additional clinical research and investigation on nanostructure-coated implants aids in efficient implementation

A polynomial equation devised using machine learning to predict the antibacterial activity of silver nanoparticles

The antibacterial activity of silver nanoparticles has been well-researched throughout the years, and massive data has been generated describing a specific nanoparticle sample and its antibacterial activity by performing laboratory experiments; however, none have utilized this data to create a means of predicting the antibacterial activity. In this paper, we developed a polynomial equation using machine learning that predicts the antibacterial activity of silver nanoparticles against S. aureus and E. coli. Only studies featuring spherical silver nanoparticles without any surface modifications that may enhance the antibacterial activity were considered, and the studies must test the antibacterial activity in terms of the number of bacterial colonies left after treatment to calculate the efficiency. The equation takes the size and amount of the nanoparticles in a particular sample as inputs and predicts its antibacterial activity in terms of the percentage of bacterial colonies left after treatment. The equation was validated for its accuracy and was found to accurately predict the antibacterial activity based on the values of the relevant features. The study is the first of its kind and contributes to the field by reducing the effort and resource consumption in laboratories and providing a simple and efficient means of predicting antibacterial activity

On the non-parametric changepoint detection of flow regimes in cyclone Amphan

The Bay of Bengal was witness to a severe cyclone named Amphan during the summer of the year 2020. The National Institute of Ocean Technology (NIOT), INDIA moorings BD08 and BD09 happened to be in the vicinity of the cyclone. The highly instrumented mooring recorded near-surface meteorological parameters like wind speed, sea surface temperature, and near-surface pressure. This article explores the possibility of using a non-parametric algorithm to identify different flow regimes using a one-month long time-series data of the near-surface parameters. The changes in the structure of the time series signal were statistically segmented using an unconstrained non-parametric algorithm. The non-parametric changepoint method was applied to time series of near-surface winds, sea surface temperature, sea level pressure, air temperature and salinity and the segmentations are consistent with visual observations. Identifying different data segments and their simple parameterization is a crucial component and relating them to different flow regimes is useful for the development of parametrization schemes in weather and climate models. The segmentations can considerably simplify the parametrization schemes when expressed as linear functions. Moreover, the usefulness of non-parametric automatic detection of data segments of similar statistical properties shall be more apparent when dealing with relatively long time series data

Diagnostic Values of Laboratory Biomarkers in Predicting a Severe Course of COVID-19 on Hospital Admission

Objective. We intend to identify differences in the clinicodemographic and laboratory findings of COVID-19 patients to predict disease severity and outcome on admission. Methods. This single-centred retrospective study retrieved laboratory and clinical data from 350 COVID-19 patients on admission, represented as frequency tables. A multivariate regression model was used to assess the statistically significant association between the explanatory variables and COVID-19 infection outcomes, where adjusted odds ratio (AOR), p value, and 95% CI were used for testing significance. Results. Among the 350 COVID-19 patients studied, there was a significant increase in the WBC count, neutrophils, aggregate index of systemic inflammation (AISI), neutrophil-to-lymphocyte ratio (dNLR), neutrophil-to-lymphocyte and platelet ratio (NLPR), monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), D-dimer, interleukin-6 (IL-6), ferritin, lactate dehydrogenase (LDH), prothrombin time (PT), glucose, urea, urea nitrogen, creatinine, alanine phosphatase (ALP), and aspartate aminotransferase (AST) and a significant decrease in lymphocytes, eosinophils, total protein, albumin, prealbumin serum, and albumin/globulin (A/G) ratio in the severe group when compared with the mild and moderate groups. However, after adjusting their age, gender, and comorbidities, WBC count (adjusted odds ratio AOR=6.888, 95% CI=1.590-29.839, p=0.010), neutrophils (AOR=5.912, 95% CI=2.131-16.402, p=0.001), and urea (AOR=4.843, 95% CI=1.988-11.755, p=0.001) were strongly associated with disease severity. Interpretation and Conclusion. On admission, WBC count, neutrophils, and urea, with their cut of values, can identify at-risk COVID-19 patients who could develop severe COVID-19

The "Woundosome" Concept and Its Impact on Procedural Outcomes in Patients With Chronic Limb-Threatening Ischemia

This editorial assembles endovascular specialists from diverse clinical backgrounds and nationalities with a global call to address key challenges to enhance revascularization in chronic limb-threatening ischemia (CLTI) patients.- Dedicated below-the-ankle (BTA) angiography and revascularization is underutilized in ischemic foot treatment. Existing guidelines do not address comprehensive BTA vessel analysis. CLTI trials also often lack data on in-line arterial flow to the ischemic lesion and BTA vessel evaluation, hindering outcome assessment.- Dedicated multi-planar angiographic evaluation of the distal microcirculation is key: Direct arterial flow or good-quality collaterals are crucial in influencing wound healing and need to be assessed diligently to the level of the distal ischemic wound territory, termed “woundosome.”- An important primary emphasis of future trials should be on validating technologies and strategies for assessing tissue perfusion before, during, and after revascularization undertaken to heal tissue loss in CLTI patients. This will allow determination of a potentially significant delta in tissue perfusion prior to and following intervention at the “woundosome” level. Once changes in arterial perfusion have been identified as positively correlated to wound healing, these could serve as a much-needed novel primary technical outcome measure for patients with tissue loss undergoing surgical, hybrid, or endovascular revascularization