Does diversity beget diversity? A scientometric analysis of over 150,000 studies and 49,000 authors published in high-impact medical journals between 2007 and 2022.

Background: Health research that significantly impacts global clinical practice and policy is often published in high-impact factor (IF) medical journals. These outlets play a pivotal role in the worldwide dissemination of novel medical knowledge. However, researchers identifying as women and those affiliated with institutions in low- and middle-income countries (LMIC) have been largely underrepresented in high-IF journals across multiple fields of medicine.

A systems biology approach for discovering the cellular and molecular aspects of psychogenic non-epileptic seizure.

Objectives: Psychogenic non-epileptic seizure (PNES) is the most common non-epileptic disorder in patients referring to epilepsy centers. Contrary to common beliefs about the disease's harmlessness, the death rate of PNES patients is similar to drug-resistant epilepsy. Meanwhile, the molecular pathomechanism of PNES is unknown with very limited related research.

FKBP5 blockade may provide a new horizon for the treatment of stress-associated disorders: An in-silico study.

Objective: We searched for, from the FDA (Food and Drug Administration-USA)-approved drugs, inhibitors of FKBP5 with tolerable adverse effect profiles (eg, mild headache, sedation, etc.) and with the ability to cross the blood brain barrier (BBB), using bio-informatics tools (in-silico). This may pave the road for designing clinical trials of such drugs in patients with functional seizures (FS) and other stress-associated disorders.

The genetic link between systemic autoimmune disorders and temporal lobe epilepsy: A bioinformatics study.

Objective: We aimed to explore the underlying pathomechanisms of the comorbidity between three common systemic autoimmune disorders (SADs) [i.e., insulin-dependent diabetes mellitus (IDDM), systemic lupus erythematosus (SLE), and rheumatoid arthritis (RA)] and temporal lobe epilepsy (TLE), using bioinformatics tools.

Identification of hub pathways and drug candidates in gastric cancer through systems biology.

Gastric cancer is the fourth cause of cancer death globally, and gastric adenocarcinoma is its most common type. Efforts for the treatment of gastric cancer have increased its median survival rate by only seven months. Due to the relatively low response of gastric cancer to surgery and adjuvant therapy, as well as the complex role of risk factors in its incidences, such as protein-pomp inhibitors (PPIs) and viral and bacterial infections, we aimed to study the pathological pathways involved in gastric cancer development and investigate possible medications by systems biology and bioinformatics tools.

Machine learning applications to differentiate comorbid functional seizures and epilepsy from pure functional seizures.

Purpose: We have utilized different methods in machine learning (ML) to develop the best algorithm to differentiate comorbid functional seizures (FS) and epilepsy from those who have pure FS.

Methods: This was a retrospective study of an electronic database of patients with seizures. All patients with a diagnosis of FS (with or without comorbid epilepsy) were studied at the outpatient epilepsy clinic at Shiraz University of Medical Sciences, Shiraz, Iran, from 2008 until 2021.