Publications by authors named "A R Baig"

Maternal stress during pregnancy, or prenatal stress, is a risk factor for neurodevelopmental disorders in offspring, including autism spectrum disorder (ASD). In ASD, dorsal striatum displays abnormalities correlating with symptom severity, but there is a gap in knowledge about dorsal striatal cellular and molecular mechanisms that may contribute. Using a mouse model, we investigated how prenatal stress impacted striatal-dependent behavior in adult offspring.

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Introduction: Civilian healthcare workers (HCW) and medical facilities are directly and indirectly impacted by armed conflict. In the Russia-Ukraine war, acute trauma care needs grew, the workforce was destabilised by HCW migrating or shifting roles to meet conflict needs, and facilities faced surge events. Chemical, biological, radiological, nuclear and explosive (CBRNE) exposure risks created unique preparedness needs.

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Ambiguous genitalia is a rare disorder where it is unclear whether an infant's external genitals are male or female. This can be attributed to various internal and external etiologies, such as androgen receptor abnormalities, gonadal abnormalities (such as gonadal dysgenesis or Klinefelter syndrome where a male has an extra X chromosome), enzymatic defects, etc. Correction of such atypical genitalia requires a multidisciplinary approach, including but not limited to surgeons and therapists.

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Objectives: To evaluate the precision and safety of a novel technique of free-hand frameless pinless AXIEM™-based navigation guided biopsy of deep-seated brain lesions in a low-middle income country.

Methods: This retrospective study included 45 patients who underwent free-hand frameless pinless AXIEM™-based navigation guided biopsy of deep-seated brain lesions using the Medtronic-Stealth S7 system over a 5-year period (January 2019 to December 2023) at the Department of Neurosurgery, Punjab Institute of Neurosciences, Lahore, Pakistan.

Results: A total of 45 patients were included in this study.

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Electrocardiography (ECG) signals are commonly used to detect cardiac disorders, with 12-lead ECGs being the standard method for acquiring these signals. The primary objective of this research is to propose a new feature engineering model that achieves both high classification accuracy and explainable results using ECG signals. To this end, a symbolic language, named Cardioish, has been introduced.

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