Ectopic Acromegaly Secondary to Bronchial Tumor; a Case Report of Rare Occurrence.

Introduction: Acromegaly is caused due to the unregulated and sustained overproduction of growth hormone (GH). The majority of the cases are caused by autonomous secretion of GH from anterior pituitary tumors. Nonetheless, in less than 1 % of the cases, the cause of autonomous secretion is secondary to ectopic growth hormone-releasing hormone (GHRH) production. Bronchial carcinoids are the most common cause of ectopic GHRH production. Case description:  A 32-year-old female presented to the clinic with a history of cough, hemoptysis, and undocumented weight loss for four years. Initial workup showed a large right main stem endobronchial mass. Transbronchial biopsy of the mass revealed a grade I neuroendocrine tumor (NET). During NET workup, a large sellar mass was incidentally found on cross-sectional imaging. The hormonal profile revealed markedly elevated insulin-like growth factor -1 (IGF-1) and mildly raised prolactin. The MRI Brain study revealed pituitary macroadenoma measuring 2 cm x 1.2 cm x 1.5 cm. The patient underwent bronchial carcinoid tumor resection, which led to normalization of serum IGF-1 and growth hormone response to an oral glucose tolerance test. Subsequent MRI brain revealed complete resolution of previously noted sellar mass. Practical implications:  This case highlights the importance of differentiating acromegaly secondary to pituitary adenoma and ectopic acromegaly. This case emphasizes the importance of keeping rare entities in the differential while assessing patients with pituitary macroadenoma

Saturation transfer difference NMR on the integral trimeric membrane transport protein GltPh determines cooperative substrate binding

UID/Multi/04378/2019 Grant no. BB/P010660/1 grant number BB/M011216/1Saturation-transfer difference (STD) NMR spectroscopy is a fast and versatile method which can be applied for drug-screening purposes, allowing the determination of essential ligand binding affinities (KD). Although widely employed to study soluble proteins, its use remains negligible for membrane proteins. Here the use of STD NMR for KD determination is demonstrated for two competing substrates with very different binding affinities (low nanomolar to millimolar) for an integral membrane transport protein in both detergent-solubilised micelles and reconstituted proteoliposomes. GltPh, a homotrimeric aspartate transporter from Pyrococcus horikoshii, is an archaeal homolog of mammalian membrane transport proteins—known as excitatory amino acid transporters (EAATs). They are found within the central nervous system and are responsible for fast uptake of the neurotransmitter glutamate, essential for neuronal function. Differences in both KD’s and cooperativity are observed between detergent micelles and proteoliposomes, the physiological implications of which are discussed.publishersversionpublishe

Transition metal ion FRET uncovers K(+) regulation of a neurotransmitter/sodium symporter

Neurotransmitter/sodium symporters (NSSs) are responsible for Na(+)-dependent reuptake of neurotransmitters and represent key targets for antidepressants and psychostimulants. LeuT, a prokaryotic NSS protein, constitutes a primary structural model for these transporters. Here we show that K(+) inhibits Na(+)-dependent binding of substrate to LeuT, promotes an outward-closed/inward-facing conformation of the transporter and increases uptake. To assess K(+)-induced conformational dynamics we measured fluorescence resonance energy transfer (FRET) between fluorescein site-specifically attached to inserted cysteines and Ni(2+) bound to engineered di-histidine motifs (transition metal ion FRET). The measurements supported K(+)-induced closure of the transporter to the outside, which was counteracted by Na(+) and substrate. Promoting an outward-open conformation of LeuT by mutation abolished the K(+)-effect. The K(+)-effect depended on an intact Na1 site and mutating the Na2 site potentiated K(+) binding by facilitating transition to the inward-facing state. The data reveal an unrecognized ability of K(+) to regulate the LeuT transport cycle

The Environment Shapes the Inner Vestibule of LeuT

Human neurotransmitter transporters are found in the nervous system terminating synaptic signals by rapid removal of neurotransmitter molecules from the synaptic cleft. The homologous transporter LeuT, found in Aquifex aeolicus, was crystallized in different conformations. Here, we investigated the inward-open state of LeuT. We compared LeuT in membranes and micelles using molecular dynamics simulations and lanthanide-based resonance energy transfer (LRET). Simulations of micelle-solubilized LeuT revealed a stable and widely open inward-facing conformation. However, this conformation was unstable in a membrane environment. The helix dipole and the charged amino acid of the first transmembrane helix (TM1A) partitioned out of the hydrophobic membrane core. Free energy calculations showed that movement of TM1A by 0.30 nm was driven by a free energy difference of similar to 15 kJ/mol. Distance measurements by LRET showed TM1A movements, consistent with the simulations, confirming a substantially different inward-open conformation in lipid bilayer from that inferred from the crystal structure

Determination of conformational changes in the leucine transporter using luminescence resonance energy transfer (LRET) spectroscopy

Die humanen Neurotransmitter-Transporter gehört zur Familie der “Solute Carrier” Nr. 6, die auf präsynaptischen Nervenendigungen sowie auf Gliazellen gefunden werden. Diese Transporter besitzen die Eigenschaft, synaptische Neurotransmission zu beendigen, indem sie die Wirkung von präsynaptisch durch Freisetzungsereignisse in den synaptischen Spalt gelangten Neurotransmitter Moleküle wiederaufnehmen. Zu diesen humanen Transportproteinen gibt es bakterielle Homologe, beispielsweise den Leucin-Transporter LeuT, der in verschiedenen unterschiedlichen Konformationen aus dem Archae-Bakterium Aquifex aeolicus kristallisiert werden konnte. Diese unterschiedlichen Konformationen umfassen eine außen-offene, außen-geschlossene und innen-offene Konformation. In der vorliegenden Arbeit wurden die Konformationsänderungen von LeuT auf atomarer Ebene untersucht. Der primäre Fokus war es, die Änderungen der intramolekularen Distanzen zu bestimmen, die mit Bewegungen des Aminoterminus assoziiert sind. Zweitens sollte der Effekt untersucht werden, den die Membranumgebung, in die LeuT eingebettet ist, spielt, unter spezieller Berücksichtigung der Dynamik der Transmembran-Domäne 1a (TM1a). Zur Untersuchung der intramolekularen Distanzen auf atomarer Ebene wurde ein experimenteller Ansatz auf Basis der Lanthaniden-gestützten Resonanz-Energie-Transfer (LRET) Spektroskopie eingesetzt. Wenn man die Daten der vorliegenden Studie mit der innen-offenen Kristallstruktur von LeuT genau betrachtet, so verbleiben die geladenen Aminosäuren der TM1a in Detergens-Mizellen relativ unbeweglich am selben Platz. Im Vergleich dazu zeigt die vorliegende Untersuchung, dass eine Bewegung von TM1a und hier insbesondere die Bewegung der geladenen Aminosäuren aus der Membranumgebung stattfindet, wenn LeuT in Membranen rekonstituiert wurde. Parallel dazu durchgeführte Molekulardynamik-Untersuchungen unterstützten die mit der LRET-Technik erhobenen Befunde, die einen klaren Unterschied zwischen Protein-Konformation in Detergens-Systemen und Membran-Rekonstitution zeigen.Human neurotransmitter transporters from the solute carrier family 6 of proteins are found on presynaptic neurons and on glial cells. These transporters inherently terminate synaptic signalling by rapid removal of neurotransmitter molecules from the synaptic cleft. The bacterial homolog transporter LeuT from Aquifex aeolicus was crystallized in various distinctive conformations, which include the outwardopen, outward-occluded and inward-open states. Here, the conformational changes in the architecture of LeuT were explored at the atomic level. The primary focus was to determine the changes in intramolecular distances associated with the N-terminus of LeuT. Secondly, the effect of the membrane environment was determined with respect to the dynamics of the transmembrane helix 1a (TM1a). The atomic scale conformational changes were experimentally addressed by employing a lanthanidebased resonance energy transfer (LRET) technique. According to the inward-open crystal structure of LeuT, the charged amino acids of TM1a in detergent micelles moved outwards unrestricted while maintaining a substrate-releasing conformation. The present study revealed that although in detergent micelles the outward movement of TM1a was unrestricted, it was constrained in a membrane environment. Molecular dynamic simulations of LeuT in detergent micelles versus a membrane environment supported the intramolecular distances obtained with LRET.submitted by Azmat SohailMedizinische Universität Wien, Diss., 201

Integrating Multiple Datasets in Google Earth Engine for Advanced Hydrological Modeling Using the Soil Conservation Service Curve Number Method

This research explores the feasibility of using cloud computing and open data sources for hydrological modeling, specifically leveraging Google Earth Engine (GEE) and the Soil Conservation Service Curve Number (SCS CN) method to estimate runoff. The SCS CN approach is commonly applied in simulating rainfall-runoff processes and is effective for estimating water inflow into rivers, lakes, and streams. Google Earth Engine provides a range of functionalities, including algorithms for rapid data manipulation and visualization, and access to extensive global remote sensing and geographic information system (GIS) datasets. The study introduces an algorithm developed in GEE to analyze precipitation data and generate antecedent moisture condition (AMC) maps. This algorithm integrates MODIS land use/land cover (LULC) data with USDA soil texture data to classify hydrological soil groups. Runoff estimation utilizes three datasets: CHIRPS, GPM, and TRMM. A thorough analysis of the rainfall-runoff relationship in the Mangla watershed from 2005 to 2015 is conducted. The study quantifies runoff estimates from each dataset and performs comparative analysis to validate the accuracy and reliability of the hydrological modeling. Over the ten-year period (2005-2015), significant fluctuations in average rainfall and runoff levels are observed, with notable seasonal patterns. The highest average precipitation of 1412.194 mm occurred in 2015, resulting in an average runoff of 215.021 mm. Conversely, 2009 recorded the lowest average precipitation of 672.808 mm and an average runoff of 78.476 mm. The accuracy of the modeled runoff observations is validated using meteorological data from the Pakistan Meteorological Department (PMD), Water and Power Development Authority (WAPDA), and Climate Forecast System Reanalysis (CFSR). In 2008, 2009, and 2010, CHIRPS consistently demonstrated better accuracy compared to GPM and TRMM, with accuracies of 90%, 79%, and 86% respectively. Additionally, a sensitivity analysis of the SCS CN model parameters reveals the effects of initial abstraction and Curve Number values on runoff estimation. In conclusion, this research enhances the understanding of hydrological processes in monsoon-affected regions and offers valuable recommendations for implementing sustainable water resource management practices

Integrating Multiple Datasets in Google Earth Engine for Advanced Hydrological Modeling Using the Soil Conservation Service Curve Number Method

This research explores the feasibility of using cloud computing and open data sources for hydrological modeling, specifically leveraging Google Earth Engine (GEE) and the Soil Conservation Service Curve Number (SCS CN) method to estimate runoff. The SCS CN approach is commonly applied in simulating rainfall-runoff processes and is effective for estimating water inflow into rivers, lakes, and streams. Google Earth Engine provides a range of functionalities, including algorithms for rapid data manipulation and visualization, and access to extensive global remote sensing and geographic information system (GIS) datasets. The study introduces an algorithm developed in GEE to analyze precipitation data and generate antecedent moisture condition (AMC) maps. This algorithm integrates MODIS land use/land cover (LULC) data with USDA soil texture data to classify hydrological soil groups. Runoff estimation utilizes three datasets: CHIRPS, GPM, and TRMM. A thorough analysis of the rainfall-runoff relationship in the Mangla watershed from 2005 to 2015 is conducted. The study quantifies runoff estimates from each dataset and performs comparative analysis to validate the accuracy and reliability of the hydrological modeling. Over the ten-year period (2005-2015), significant fluctuations in average rainfall and runoff levels are observed, with notable seasonal patterns. The highest average precipitation of 1412.194 mm occurred in 2015, resulting in an average runoff of 215.021 mm. Conversely, 2009 recorded the lowest average precipitation of 672.808 mm and an average runoff of 78.476 mm. The accuracy of the modeled runoff observations is validated using meteorological data from the Pakistan Meteorological Department (PMD), Water and Power Development Authority (WAPDA), and Climate Forecast System Reanalysis (CFSR). In 2008, 2009, and 2010, CHIRPS consistently demonstrated better accuracy compared to GPM and TRMM, with accuracies of 90%, 79%, and 86% respectively. Additionally, a sensitivity analysis of the SCS CN model parameters reveals the effects of initial abstraction and Curve Number values on runoff estimation. In conclusion, this research enhances the understanding of hydrological processes in monsoon-affected regions and offers valuable recommendations for implementing sustainable water resource management practices

Benchmarking performance of document level classification and topic modeling

Text classification of low resource language is always a trivial and challenging problem. This paper discusses the process of Urdu news classification and Urdu documents similarity. Urdu is one of the most famous spoken languages in Asia. The implementation of computational methodologies for text classification has increased over time. However, Urdu language has not much experimented with research, it does not have readily available datasets, which turn out to be the primary reason behind limited research and applying the latest methodologies to the Urdu. To overcome these obstacles, a medium-sized dataset having six categories is collected from authentic Pakistani news sources. Urdu is a rich but complex language. Text processing can be challenging for Urdu due to its complex features as compared to other languages. Term frequency-inverse document frequency (TFIDF) based term weighting scheme for extracting features, chi-2 for selecting essential features, and Linear discriminant analysis (LDA) for dimensionality reduction have been used. TFIDF matrix and cosine similarity measure have been used to identify similar documents in a collection and find the semantic meaning of words in a document FastText model has been applied. The training-test split evaluation methodology is used for this experimentation, which includes 70% for training data and 30% for testing data. State-of-the-art machine learning and deep dense neural network approaches for Urdu news classification have been used. Finally, we trained Multinomial Naïve Bayes, XGBoost, Bagging, and Deep dense neural network. Bagging and deep dense neural network outperformed the other algorithms. The experimental results show that deep dense achieves 92.0% mean f1 score, and Bagging 95.0% f1 score