Publications by authors named "H M Ali"

The design of electrically conductive textiles appears to be a promising approach to combat the existing challenge of deaths caused by severe cold climates around the globe. However, reports on the scalable fabrication of tolerant conductive textiles maintaining a low electrical resistance with an ability for unperturbed and prolonged performance are scarce. Here, a breathable and wrappable water-repellent conductive textile (water-repellent CT) with electrothermal and photothermal conversion abilities at low external voltage and in weak solar light is introduced, respectively.

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The Mediterranean Sea is an intercontinental marine environment renowned for its biodiversity and ecological significance. However, it is also one of the most polluted seas globally with significant levels of microplastics and heavy metals among other emerging contaminants. In Lebanon, inadequate waste management infrastructure and unregulated industrial discharges have exacerbated water quality deterioration by introducing these complex contaminants into surface and seawater.

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The role of p53 expression in colorectal cancer (CRC) was investigated in this immunohistochemical analysis of 110 CRC patients. The study aimed to explore the relationship between p53 expression and clinicopathological features, such as tumor grade, size, lymph node involvement, and molecular subtypes. The mean age of patients was 52.

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Background: Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) mainly affects the respiratory tract, but different organs may be involved including the kidney. Data on acute kidney injury (AKI) in critical forms of coronavirus disease 2019 (COVID-19) are scarce. We aimed to assess the incidence, risk factors and prognostic impact of AKI complicating critical forms of COVID-19.

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Predicting the outcome of a kidney transplant involving a living donor advances donor decision-making donors for clinicians and patients. However, the discriminative or calibration capacity of the currently employed models are limited. We set out to apply artificial intelligence (AI) algorithms to create a highly predictive risk stratification indicator, applicable to the UK's transplant selection process.

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