Femoral intramedullary nail extraction using the kissing nail technique when primary measures fail.

Introduction: Intramedullary femoral nails (IMFNs) need to be removed for subsequent joint replacement, refracture, nonunion, or infection. The tools used to extract newer IMFNs may not be suited for removal of older implants, especially if broken. The purpose of this study was to describe a novel technique in femoral nail extraction when primary measures fail and a report on 6 cases where it was used.

Unraveling tea and coffee consumption effects on cardiovascular diseases risk factors: A narrative review.

Backgrounds And Aims: Daily lifestyle plays a vital role in modifying the risk for cardiovascular diseases (CVDs). Our daily life isn't inseparable from nutrition intake. As such, tea and coffee are often regarded as the most consumed beverages worldwide.

Case report: Successful treatment of renal-limited thrombotic microangiopathy secondary to chronic lymphocytic leukemia.

Thrombotic microangiopathy (TMA) is a rare renal complication of patients with chronic lymphocytic leukemia (CLL) and is often associated with peripheral features. We present the first case of CLL patients with renal-limited TMA. A 70-year-old female patient with a history of well-controlled type 2 diabetes and baseline albuminuria of 87.

Working memory enhancement using real-time phase-tuned transcranial alternating current stimulation.

Background: Prior work has shown that transcranial alternating current stimulation (tACS) of parietooccipital alpha oscillations (8-14 Hz) can modulate working memory (WM) performance as a function of the phase lag to endogenous oscillations. However, leveraging this effect using real-time phase-tuned tACS has not been feasible so far due to stimulation artifacts preventing continuous phase tracking.

Objectives And Hypothesis: We aimed to develop a system that tracks and adapts the phase lag between tACS and ongoing parietooccipital alpha oscillations in real-time.

Machine learning prediction of methane, nitrogen, and natural gas mixture viscosities under normal and harsh conditions.

The accurate estimation of gas viscosity remains a pivotal concern for petroleum engineers, exerting substantial influence on the modeling efficacy of natural gas operations. Due to their time-consuming and costly nature, experimental measurements of gas viscosity are challenging. Data-based machine learning (ML) techniques afford a resourceful and less exhausting substitution, aiding research and industry at gas modeling that is incredible to reach in the laboratory.