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Author(s): Trupti V. Deshpande (corresponding author) [1]; Pranav Dorwal [1]; Akshay A Anikhindi [2]; Nishant Tiwari [3]; Dharmendar Jain [1]; Simmi Mehra [1]; Ashok Vaid [4]; Vimarsh Raina [1] Introduction [...], Background: To study the prevalence of genetic mutations in patients with gastrointestinal stromal tumors (GIST) using next-generation sequencing at a tertiary care center in northern India. Methods: We performed genotypic analysis of histologically and immunohistochemically diagnosed GIST using Ion AmpliSeq Cancer Hotspot Panel v2 to identify mutations on resected biopsy samples. Results: In our cohort of 41 patients, we observed a variety of genetic mutations. The corresponding prevalence for those mutations was Tumour Protein 53 (TP53) 90, KIT Protooncogene receptor tyrosine kinase (KIT) 88, Platelet Derived Growth Factor Receptor (PDGFRA) 78, HRas Proto-oncogene (HRAS) 61, Phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA) 56, Kristen rat sarcoma viral oncogene homolog (KRAS) 54, NRas Proconcogene GTPase (NRAS) 20, Cyclin-dependent kinase inhibitor 2A (CDKN2A) 15, and B-Raf proto-oncogene (BRAF) 7. Conclusion: GISTs host a variety of mutations that need to be further investigated for their interplay and overall effect on the Indian population so that we can tailor treatment for better prognosis. The incidence of GIST is rising worldwide. Advances in technology have made tailored theranostics a possibility in managing GIST. Detailed genetic presentation of GIST is still unclear. Next-generation sequencing has provided us with an excellent tool to prospectively analyze the genetic makeup of GIST and most of the tumors. The present study opens up a huge possibility for using molecular makers for risk stratification of GIST and for theranostics of such tumors. Keywords: GIST, KIT, NGS

Magnetic refrigeration is an active research area considering its potential to replace gas-based refrigeration systems, which are detrimental to the environment owing to chlorofluorocarbon emissions. Heusler alloys such as Ni2MnGa are known for their magnetocaloric effect, and addition of elements like Cu has increased the refrigeration capacity of the system. Magnetocaloric properties are a strict function of composition, and few alloy compositions in Mn–Ni–Cu–Ga can exhibit enhanced magnetocaloric properties. Most of the previous works have focused on Mn–Ni–Cu–Ga, and the enhancement of magnetocaloric properties is attributed to adding Cu. However, no magnetocaloric material has been reported in the Mn–Ni–Cu system, which could potentially show benefit from Cu addition. Hence, in the present study, thermodynamic optimization of the Mn–Ni–Cu-based ternary system and its three sub-binaries, Mn–Cu, Mn–Ni, and Ni–Cu, are performed using the CALculation of PHAse Diagram approach. In order to capture the liquidus in the Mn–Ni system and congruent transformation, a few alloy compositions were prepared and analyzed using differential thermal analysis. Using the current optimized database, temperatures are calculated as a function of Mn, Ni, and Cu compositions to ascertain the phase transition between the austenite and martensite phases, which occur around room temperature. [ABSTRACT FROM AUTHOR]

Two-dimensional (2D) chromium telluride Cr2Te3 exhibits strong ferromagnetic ordering with high coercivity at low temperatures and paramagnetic behavior when approaching room temperature. The spin states of monolayer Cr2Te3 show ferromagnetic ordering in the ground state. In situ Raman analysis reveals a reversible structure transformation and hence proves ferromagnetic to paramagnetic transition during low-temperature heating cycles (0–25 °C). The magnetic phase transition near room temperature in 2D Cr2Te3 prompted an exploratory study of these layered materials for energy application. We demonstrate that the low-temperature ferromagnetic behavior can be used to magnetically deice material surfaces using an external magnetic source, avoiding the use of harsh chemicals and high temperatures. The hydrophobic nature and dipole interactions of H2O molecules with the surface of the 2D Cr2Te3 coating aid in the condensation of ice droplets formed on the surfaces. First-principles calculations also confirm the observed crystal structure, surface interaction, and magnetic properties of 2D Cr2Te3. [ABSTRACT FROM AUTHOR]

Duchenne muscular dystrophy (DMD) is a rare genetic disorder caused by the absence of a fully functional dystrophin protein in myocytes. In skeletal muscle, the lack of dystrophin ultimately results in muscle wasting and the replacement of myocytes with fatty or fibrous tissues. In the heart, cardiomyocytes eventually fail and cause fatal cardiomyopathy. We present a case of a male patient and his younger brother with a maternally inherited inverted insertion of approximately 306 kb of chromosome 10 in the deep intronic region between exons 44 and 45 of the DMD gene, leading to Duchenne muscular dystrophy. Chromosomal microarray, comprehensive muscular dystrophy genetic testing, and whole exome sequencing were negative. Targeted transcriptome RNA sequencing at an external lab showed no aberrant splicing. Research whole genome sequencing identified the copy number gain and insertion. Subsequent reanalysis of the RNA sequencing data showed possible aberrant splicing involving DMD exons 44–45, and research RNA sequencing revealed a fusion between the DMD gene on the minus strand of chromosome X and the PFKFB3 gene on the plus strand of chromosome 10. We demonstrate that whole genome sequencing can be valuable for identifying intronic events in the DMD gene previously undetected or not reported by traditional clinical testing. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease, 2019 (COVID – 19) evolved as an unprecedented pandemic. The severe acute respiratory syndrome-corona virus-2 (SARS-CoV-2) infection has been associated with significantly deranged coagulation parameters and increased incidence of thrombotic events. Deranged coagulation parameters, such as D-dimers and fibrin degradation products, can indicate a poor prognosis, and their measurement will help stratify the patients according to the disease severity, need of intensive care unit admission, and prediction of the clinical course. Gaps in understanding the natural history of the disease cause difficulties in tailoring therapies and optimizing the management of patients. Lack of specific treatment further complicates this situation. While thrombotic events can cause significant morbidity and mortality in patients, a focused approach to the prevention and treatment of venous thromboembolism (VTE) can, to a great extent, decrease the disease burden caused by thrombotic diseases. Pharmacological prophylactic anticoagulants and mechanical therapies such as pneumatic compression devices can help prevent venous thromboembolism and other thrombotic events. Thrombotic events due to COVID-19, their prevention and management, are the focus of this paper, with the prospect of providing insights into this relatively unexplored area.

This research introduces an innovative approach by utilising rock glaciers (RGs) as a proxy for mapping debris‐covered glaciers (DCGs). This approach focuses on the interconnected nature of glaciers, DCGs and RGs in a continuum where DCGs can transform into RGs over time due to various processes. This study utilises six machine learning models—logistic regression (LR), support vector machine (SVM), K‐nearest neighbour (KNN), Naïve Bayes (NB), decision tree (DT) and random forest (RF)—combined with multispectral satellite data (Sentinel‐2 and Landsat 8) and topographical data derived from ALOS PALSAR DEM. Performance metrics such as accuracy, area under the curve (AUC) score, precision, recall and F1‐score were evaluated to assess model performance. This detailed mapping provides a precise estimation of the extent of DCGs in the Kinnaur district. The estimated DCG areas revealed intriguing variation across models, with RF (9.71%), KNN (9.67%) and NB (9.41%) yielding similar predictions. SVM (11.61%) projected a slightly larger DCG area, whereas DT (5.54%) and LR (25.55%) provided contrasting results. Validation against high‐resolution satellite images, Google Earth images and glacier inventories confirmed the accuracy and reliability of our approach. Based on our findings for our specific study, the most effective method for mapping DCGs is RF, followed by KNN, NB, DT and SVM. The combination of machine learning models and RG data presents a novel and promising approach to remote sensing‐based DCG mapping, with potential applications for other regions and broader environmental studies. [ABSTRACT FROM AUTHOR]

Rock glaciers are geomorphological features often used as a visible expression of mountain permafrost. These are creeping ice-debris with distinct ridge and furrow structures on the surface with a steep frontal slope. Rock glaciers, being the valuable past permafrost indicators, also have utmost hydrological significance in near future. Therefore, mapping of rock glaciers is an important step in order to understand permafrost regimes better. The Himalayas have large occurrences of these features and this study in Himachal Himalayas complied 789 rock glaciers, covering an area of about 336.2 km2. Different labels based on genesis, location, shape, form, surface relief and activity revealed rock glaciers were mainly derived from talus slopes (239) and exhibited tongue shape (377), primarily found in cirques (531). Most of them were classified as simple units (603) with well-developed surface relief (387), and they were found to be predominantly intact (760). The topographical parameters suggest majority of the rock glaciers are located between 4000 and 4800 m with mean elevation to be 4635 m. These rock glaciers are present at gentle to steep slope gradient (0 to 45°) with curvature ranging between −3.5 and 4.5, and majority showing convex curvature. The slope aspect conducive for formation of rock glaciers in Himachal is northerly (N, NW, NE). Principal geology for these rock glaciers belongs to slate, phyllite, quartzarenite, limestone and meta basics. The climatic parameters and indices also affect the rock glaciers occurrence significantly. The mean land surface temperature (LST) for majority rock glaciers lies between 0 and −15°C. While, the mean NDSI of all the rock glaciers varies from 0.04 to 0.68 and mean NDVI varies from −0.06 to 0.08. Overall, the inventory along with labels is a valuable database for understanding the distribution and characteristics of rock glaciers in the Himachal Himalayas. [ABSTRACT FROM AUTHOR]