Pain determinants and quality of life in Guillain-Barre syndrome: a prospective cohort study.

Background: Pain is a serious manifestation in both the acute and chronic stages of Guillain-Barre syndrome (GBS). We evaluated the frequency, characteristics and associated factors of pain and its impact on quality of life (QoL) among patients with GBS.

Methods: We enrolled 644 patients with GBS from prospective cohort studies in Bangladesh conducted between 2010 and 2024.

Morphometric parameters based prioritization of watersheds for soil erosion risk in Upper Jhelum Sub-catchment, India.

Soil erosion is the inherent and destructive threat affecting agricultural production and livelihood of million mouths. The increased frequency of floods and land use/land cover changes has made Upper Jhelum Sub-catchment susceptible to soil erosion risk. Morphometric based watershed prioritization for soil erosion risk may help in sustainable management of natural resources.

Management of Guillain-Barré syndrome in Bangladesh: Clinical practice, limitations and recommendations for low- and middle-income countries.

Background And Aims: Considerable variation in clinical practice for management of Guillain-Barré syndrome (GBS) has been observed worldwide. Diagnosis and treatment are challenging in low- and middle-income countries (LMIC) due to lack of facilities and treatment availability. We aimed to evaluate current clinical practice and limitations and to provide recommendation for GBS management in low-resource settings.

Impact of river flow modification on wetland hydrological and morphological characters.

A good number of researchers investigated the impact of flow modification on hydrological, ecological, and geomorphological conditions in a river. A few works also focused on hydrological modification on wetland with some parameters but as far the knowledge is concerned, linking river flow modification to wetland hydrological and morphological transformation following an integrated modeling approach is often lacking. The current study aimed to explore the degree of hydrological alteration in the river and its effect on downstream riparian wetlands by adopting advanced modeling approaches.

Effects of lockdown due to COVID-19 outbreak on air quality and anthropogenic heat in an industrial belt of India.

Highly urbanized and industrialized Asansol Durgapur industrial belt of Eastern India is characterized by severe heat island effect and high pollution level leading to human discomfort and even health problems. However, COVID-19 persuaded lockdown emergency in India led to shut-down of the industries, traffic system, and day-to-day normal work and expectedly caused changes in air quality and weather. The present work intended to examine the impact of lockdown on air quality, land surface temperature (LST), and anthropogenic heat flux (AHF) of Asansol Durgapur industrial belt.

Selecting optimal conditioning parameters for landslide susceptibility: an experimental research on Aqabat Al-Sulbat, Saudi Arabia.

Landslides and other disastrous natural catastrophes jeopardise natural resources, assets, and people's lives. As a result, future resource management will necessitate landslide susceptibility mapping (LSM) using the best conditioning factors. In Aqabat Al-Sulbat, Asir province, Saudi Arabia, the goal of this study was to find optimal conditioning parameters dependent hybrid LSM.

How far spatial resolution affects the ensemble machine learning based flood susceptibility prediction in data sparse region.

Although the effect of digital elevation model (DEM) and its spatial resolution on flood simulation modeling has been well studied, the effect of coarse and finer resolution image and DEM data on machine learning ensemble flood susceptibility prediction has not been investigated, particularly in data sparse conditions. The present work was, therefore, to investigate the performance of the resolution effects, such as coarse (Landsat and SRTM) and high (Sentinel-2 and ALOS PALSAR) resolution data on the flood susceptible models. Another motive of this study was to construct very high precision and robust flood susceptible models using standalone and ensemble machine learning algorithms.