BBBper: A Machine Learning-based Online Tool for Blood-Brain Barrier (BBB) Permeability Prediction.

AIMS: Neuronal disorders have affected more than 15% of the world's population, signifying the importance of continued design and development of drugs that can cross the Blood-Brain Barrier (BBB). BACKGROUND: BBB limits the permeability of external compounds by 98% to maintain and regulate brain homeostasis. Hence, BBB permeability prediction is vital to predict the activity of a drug-like substance. OBJECTIVE: Here, we report about developing BBBper (Blood-Brain Barrier permeability prediction) using machine learning tool. METHOD: A supervised machine learning-based online tool, based on physicochemical parameters to predict the BBB permeability of given chemical compounds was developed. The user-end webpage was developed in HTML and linked with back-end server by a python script to run user queries and results. RESULT: BBBper uses a random forest algorithm at the back end, showing 97% accuracy on the external dataset, compared to 70-92% accuracy of currently available web-based BBB permeability prediction tools. CONCLUSION: The BBBper web tool is freely available at http://bbbper.mdu.ac.in.

Analyzing the structural, optoelectronic, and thermoelectric properties of InGeX3 (X = Br) perovskites via DFT computations.

The Electronic and optical properties of InGeX3(X = Cl, Br) were examined by adopting the density functional theory (DFT) approach. We applied the GGA + Trans-Blaha modified Becke-Johnson (TB-mBJ) technique to acquire the precise bandgap of 1.52 and 0.98 eV of the compounds InGeX3(X = Cl, Br) respectively which suggests the direct bandgap at (M-M). The stability of the material is confirmed by the formation energy (- 2.83 = Cl; - 2.35 = Br) and Mechanical stability. Primarily elastic constants were extracted for each of the materials under scrutiny, and these values then served to gauge all of the materials' mechanical properties. The assessed Poisson's and Pugh's ratios for the materials InGeCl3 and InGeBr3 were verified to identify the degree of ductility. The quasi-harmonic Debye model additionally covers the temperature and pressure dependence on thermodynamic parameters, particularly volume, specific heat capacity (Cv) at constant volume, and the Gruneisen parameter (γ) in the range of 0-800 K and 0-5 GPa. It is anticipated that InGeCl3 and InGeBr3 will have static dielectric constants of 4.01 and 5.74, respectively. InGeX3(X = Cl, Br) also reveals significant absorption in the high UV spectrum. The thermoelectric properties have also been calculated vdata-element-id="9QNfR3VHbcMHX_W0fJCYp" data-element-type="html" style="display: initial; visibility: initial; opacity: initial; clip-path: initial; position: relative; float: left; top: 0px; left: 0px; z-index: 1 !important; pointer-events: none;" />ia boltztrap2 code using a k mesh of around 1,50,000 points.

DFT simulations of the elastic, optoelectronic, and thermoelectric attributes of AOsCl3 (A = K, Rb), a robust and environmentally friendly perovskites for green energy implications.

Halide perovskites are an intriguing renewable energy materials that may assist in addressing the world's energy scarcity. The Goldsmith tolerance factor (0.99 and 1.00) and negative formation energy guarantee the examined materials' structural and thermodynamic stabilities. The Poisson's and Pugh's ratio proves the reviewed materials' ductile nature. K/RbOsCl3 has electronic band gaps of 1.37 eV/1.39 eV and the highest light absorption in the ultraviolet and visible areas, enhancing its efficacy for solar cells and other optoelectronics technologies. The highest Seebeck coefficient and electrical conductivity render these materials feasible for thermoelectric properties. KOsCl3 and RbOsCl3 exemplify figures of merit of 0.89 and 0.88, respectively, at the ambient temperature. Additionally, the Debye model was implemented to figure out thermodynamic parameters such as heat capacity, thermal expansion, Debye temperature, and Grüneisen parameter.

Comparative genomics of two protozoans Dictyostelium discoideum and Plasmodium falciparum reveals conserved as well as distinct regulatory pathways crucial for exploring novel therapeutic targets for Malaria.

Plasmodium falciparum, which causes life-threatening cerebral malaria has rapidly gained resistance against most frontline anti-malarial drugs, thereby generating an urgent need to develop novel therapeutic approaches. Conducting in-depth investigations on Plasmodium in its native form is challenging, thereby necessitating the requirement of an efficient model system. In line, mounting evidence suggests that Dictyostelium discoideum retains both conformational and functional properties of Plasmodium proteins, however, the true potential of Dictyostelium as a host system is not fully explored. In the present study, we have exploited comparative genomics as a tool to extract, compare, and curate the extensive data available on the organism-specific databases to evaluate if D. discoideum can be established as a prime model system for functional characterization of P. falciparum genes. Through comprehensive in silico analysis, we report that despite the presence of adaptation-specific genes, the two display noteworthy conservation in the housekeeping genes, signaling pathway components, transcription regulators, and post-translational modulators. Furthermore, through orthologue analysis, the known, potential, and novel drug target genes of P. falciparum were found to be significantly conserved in D. discoideum. Our findings advocate that D. discoideum can be employed to express and functionally characterize difficult-to-express P. falciparum genes.

Novel molecular inhibitor design for Plasmodium falciparum Lactate dehydrogenase enzyme using machine learning generated library of diverse compounds.

Generative machine learning models offer a novel strategy for chemogenomics and de novo drug design, allowing researchers to streamline their exploration of the chemical space and concentrate on specific regions of interest. In cases with limited inhibitor data available for the target of interest, de novo drug design plays a crucial role. In this study, we utilized a package called 'mollib,' trained on ChEMBL data containing approximately 365,000 bioactive molecules. By leveraging transfer learning techniques with this package, we generated a series of compounds, starting from five initial compounds, which are potential Plasmodium falciparum (Pf) Lactate dehydrogenase inhibitors. The resulting compounds exhibit structural diversity and hold promise as potential novel Pf Lactate dehydrogenase inhibitors.

Exploring the structural, mechanical, electronic, thermodynamic and thermoelectric properties of caesium based ABX3 perovskite CsOsX3 (X: Cl, Br).

Here, we have investigated properties of caesium based halide perovskites with the help of density functional theory. We employed the generalized gradient approximation (GGA) functional to determine the structural characteristics. Conversely, for evaluating the electronic and thermoelectric properties of these materials we utilized the modified Becke and Johnson (mBJ) potential functional. Our findings indicate that these materials exhibit semiconducting properties. Furthermore, our analysis of the transport properties using the Boltzmann transport equation indicates that the studied perovskites are well-suited for thermoelectric applications.

Computational Design of Novel Tau-Tubulin Kinase 1 Inhibitors for Neurodegenerative Diseases.

The tau-tubulin kinase 1 (TTBK1) protein is a casein kinase 1 superfamily member located at chromosome 6p21.1. It is expressed explicitly in the brain, particularly in the cytoplasm of cortical and hippocampal neurons. TTBK1 has been implicated in the phosphorylation and aggregation of tau in Alzheimer's disease (AD). Considering its significance in AD, TTBK1 has emerged as a promising target for AD treatment. In the present study, we identified novel TTBK1 inhibitors using various computational techniques. We performed a virtual screening-based docking study followed by E-pharmacophore modeling, cavity-based pharmacophore, and ligand design techniques and found ZINC000095101333, LD7, LD55, and LD75 to be potential novel TTBK1 lead inhibitors. The docking results were complemented by Molecular Mechanics/Generalized Born Surface Area (MMGBSA) calculations. The molecular dynamics (MD) simulation studies at a 500 ns scale were carried out to monitor the behavior of the protein toward the identified ligands. Pharmacological and ADME/T studies were carried out to check the drug-likeness of the compounds. In summary, we identified a new series of compounds that could effectively bind the TTBK1 receptor. The newly designed compounds are promising candidates for developing therapeutics targeting TTBK1 for AD.

Reporting multitargeted potency of Tiaprofenic acid against lung cancer: Molecular fingerprinting, MD simulation, and MTT-based cell viability assay studies.

Lung Cancer (LC) is among the most death-causing cancers, has caused the most destruction and is a gender-neutral cancer, and WHO has kept this cancer on its priority list to find the cure. We have used high-throughput virtual screening, standard precision docking, and extra precise docking for extensive screening of Drug Bank compounds, and the uniqueness of this study is that it considers multiple protein targets of prognosis and metastasis of LC. The docking and MM\GBSA calculation scores for the Tiaprofenic acid (DB01600) against all ten proteins range from -8.422 to -5.727 kcal/mol and - 47.43 to -25.72 kcal/mol, respectively. Also, molecular fingerprinting helped us to understand the interaction pattern of Tiaprofenic acid among all the proteins. Further, we extended our analysis to the molecular dynamic simulation in a neutralised SPC water medium for 100 ns. We analysed the root mean square deviation, fluctuations, and simulative interactions among the protein, ligand, water molecules, and protein-ligand complexes. Most complexes have shown a deviation of <2 Å as cumulative understanding. Also, the fluctuations were lesser, and only a few residues showed the fluctuation with a huge web of interaction between the protein and ligand, providing an edge that supports that the protein and ligand complexes were stable. In the MTT-based Cell Viability Assay, Tiaprofenic Acid exhibited concentration-dependent anti-cancer efficacy against A549 lung cancer cells, significantly reducing viability at 100 µg/mL. These findings highlight its potential as a therapeutic candidate, urging further exploration into the underlying molecular mechanisms for lung cancer treatment.

Probing the opto-electronic, phonon spectrum, and thermoelectric properties of lead-free fluoride perovskites A2GeSnF6 (A = K, Rb, Cs) for energy harvesting devices.

The present work employs density functional theory to explore the structural, optoelectronic, and thermoelectric attributes of the halide-based double perovskite A2GeSnF6 (A = K, Rb, and Cs) compounds. The stable phonon dispersion spectrum affirms dynamical stability, whereas the enthalpy of formation and tolerance factor evaluated collectively verify structural stability. Considering the Tran Blaha modified Becke Johnson potentials (mBJ), the predicted direct band gaps along the symmetry point are 3.19 eV for K2GeSnF6, 3.16 eV for Rb2GeSnF6 and 3.12 eV Cs2GeSnF6. According to an in-depth examination of the optoelectronic features, A2GeSnF6 (A = K, Rb, and Cs), double perovskites are assuring contenders for optoelectronic devices due to their suitable bandgap. The extremely high figure of merit values (0.94-0.97) obtained from the numerical calculation of power factor and thermal conductivity suggest the intriguing prospects of these compositions for thermoelectric devices. These studies offer a perceptive comprehension of the materials for their potential applications in the future.

Exploring the multifaceted properties: electronic, magnetic, Curie temperature, elastic, thermal, and thermoelectric characteristics of gadolinium-filled PtSb3 skutterudite.

The investigation of binary and filled skutterudite structures, particularly PtSb3 and GdPt4Sb12, has gained significant attention, becoming a focal point in scientific research. This comprehensive report delves into the intrinsic characteristics of these structures using Density Functional Theory (DFT). Initially, we assess the structural stability of PtSb3 and GdPt4Sb12 by examining their total ground state energy and cohesive energy, employing the Brich Murnaghan equation of state to determine stability in various configurations. Further insights are gained by exploring second-order elastic constants (SOEC's) to extend our understanding of structural stability. The electronic structures are then meticulously defined through a quantum mechanical treatment, employing a combination of two distinct spin-polarized approximation schemes: Perdew-Burke-Ernzerhof Generalised Gradient Approximation (PBE-GGA) and Tran-Blaha modified Becke-Johnson (TB-mBJ). The resulting band structures reveal a symmetry in electronic behavior, showcasing spin-magnetic moments of 3 µB and 7.58 µB per formula unit, with the primary contributions emanating from the Pt 3d and Pt4+ 3d-transition elements. To gauge thermal stability, we evaluate the phonon-dependent Grüneisen parameter (γ) across specific temperature ranges. The study extends to exploring transport properties as a function of chemical potential (µ - EF) at various temperatures. The findings suggest that these designed materials hold substantial potential for diverse applications, particularly in conventional spin-based and thermoelectric technologies. The comprehensive insights obtained through this investigation pave the way for a deeper understanding and broader implications in various technological domains.

MmcA is an electron conduit that facilitates both intracellular and extracellular electron transport in Methanosarcina acetivorans.

Methanogens are a diverse group of Archaea that obligately couple energy conservation to the production of methane. Some methanogens encode alternate pathways for energy conservation, like anaerobic respiration, but the biochemical details of this process are unknown. We show that a multiheme c-type cytochrome called MmcA from Methanosarcina acetivorans is important for intracellular electron transport during methanogenesis and can also reduce extracellular electron acceptors like soluble Fe3+ and anthraquinone-2,6-disulfonate. Consistent with these observations, MmcA displays reversible redox features ranging from -100 to -450 mV versus SHE. Additionally, mutants lacking mmcA have significantly slower Fe3+ reduction rates. The mmcA locus is prevalent in members of the Order Methanosarcinales and is a part of a distinct clade of multiheme cytochromes that are closely related to octaheme tetrathionate reductases. Taken together, MmcA might act as an electron conduit that can potentially support a variety of energy conservation strategies that extend beyond methanogenesis.

Exploring the structural, mechanical, magneto-electronic and thermophysical properties of f electron based XNpO3 perovskites (X = Na, Cs, Ca, Ra).

Here, we present systematic investigation of the structural and mechanical stability, electronic profile and thermophysical properties of f-electron based XNPO3 (X = Na, Cs, Ca, Ra) perovskites by first principles calculations. The structural optimization, tolerance factor criteria depicts the cubic structural stability of these alloys. Further, the stability of these materials is also determined by the cohesive and formation energy calculations along with mechanical stability criteria. The electronic structure is explored by calculating band structure and density of states which reveal the well-known half-metallic nature of the materials. Further, we have calculated different thermodynamic parameters including specific heat capacity, thermal expansion, Gruneisen parameter and their variation with temperature and pressure. The thermoelectric effectiveness of these materials is predicted in terms of Seebeck coefficient, electrical conductivity and power factor. All-inclusive we can say that calculated properties of these half-metallic materials extend their route in spintronics, thermoelectric and radioisotope generators device applications.

Machine learning-assisted prediction of trabeculectomy outcomes among patients of juvenile glaucoma by using 5-year follow-up data.

OBJECTIVE: To develop machine learning (ML) models, using pre and intraoperative surgical parameters, for predicting trabeculectomy outcomes in the eyes of patients with juvenile-onset primary open-angle glaucoma (JOAG) undergoing primary surgery. SUBJECTS: The study included 207 JOAG patients from a single center who met the following criteria: diagnosed between 10 and 40 years of age, with an IOP of >22 mmHg in the eyes on two or more occasions, open angle on gonioscopy in both eyes, with glaucomatous optic neuropathy, and requiring a trabeculectomy for IOP control. Only the patients with a minimum 5-year follow-up after surgery were included in the study. METHODS: A successful surgical outcome was defined as IOP ≤18 mmHg (criterion A) or 50% reduction in IOP from baseline (criterion B) 5 years after trabeculectomy. Feature selection techniques were used to select the most important contributory parameters, and tenfold cross-validation was used to evaluate model performance. The ML models were evaluated, compared, and prioritized based on their accuracy, sensitivity, specificity, Matthew correlation coefficient (MCC) index, and mean area under the receiver operating characteristic curve (AUROC). The prioritized models were further optimized by tuning the hyperparameters, and feature contributions were evaluated. In addition, an unbiased relationship analysis among the parameters was performed for clinical utility. RESULTS: Age at diagnosis, preoperative baseline IOP, duration of preoperative medical treatment, Tenon's thickness, scleral fistulation technique, and intraoperative mitomycin C (MMC) use, were identified as the main contributing parameters for developing efficient models. The three models developed for a consensus-based outcome to predict trabeculectomy success showed an accuracy of >86%, sensitivity of >90%, and specificity of >74%, using tenfold cross-validation. The use of intraoperative MMC and a punch for scleral fistulation compared to the traditional excision with scissors were significantly associated with long-term success of trabeculectomy. CONCLUSION: Optimizing surgical parameters by using these ML models might reduce surgical failures associated with trabeculectomy and provide more realistic expectations regarding surgical outcomes in young patients.

MOF magic: zirconium-based frameworks in theranostic and bio-imaging applications.

Over the past two decades, metal-organic frameworks (MOFs) have garnered substantial scientific interest across diverse fields, spanning gas storage, catalysis, biotechnology, and more. Zirconium, abundant in nature and biologically relevant, offers an appealing combination of high content and low toxicity. Consequently, Zr-based MOFs have emerged as promising materials with significant potential in biomedical applications. These MOFs serve as effective nanocarriers for controlled drug delivery, particularly for challenging antitumor and retroviral drugs in cancer and AIDS treatment. Additionally, they exhibit prowess in bio-imaging applications. Beyond drug delivery, Zr-MOFs are notable for their mechanical, thermal, and chemical stability, making them increasingly relevant in engineering. The rising demand for stable, non-toxic Zr-MOFs facilitating facile nanoparticle formation, especially in drug delivery and imaging, is noteworthy. This review focuses on biocompatible zirconium-based metal-organic frameworks (Zr-MOFs) for controlled delivery in treating diseases like cancer and AIDS. These MOFs play a key role in theranostic approaches, integrating diagnostics and therapy. Additionally, their utility in bio-imaging underscores their versatility in advancing medical applications.

MicroRNA Signatures for Pancreatic Cancer and Chronic Pancreatitis: Expression Profiling by NGS.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a deadly disease due to the lack of early detection. Because chronic pancreatitis (CP) patients are a high-risk group for pancreatic cancer, this study aimed to assess the differential miRNA profile in pancreatic tissue of patients with CP and pancreatic cancer. METHODS: MiRNAs were isolated from formalin-fixed paraffin-embedded pancreatic tissue of 22 PDAC patients, 18 CP patients, and 10 normal pancreatic tissues from autopsy (C) cases and processed for next-generation sequencing. Known and novel miRNAs were identified and analyzed for differential miRNA expression, target prediction, and pathway enrichment between groups. RESULTS: Among the miRNAs identified, 166 known and 17 novel miRNAs were found exclusively in PDAC tissues, while 106 known and 10 novel miRNAs were found specifically in CP tissues. The pathways targeted by PDAC-specific miRNAs and differentially expressed miRNAs between PDAC versus CP tissues and PDAC versus control tissues were the proteoglycans pathway, Hippo signaling pathway, adherens junction, and transforming growth factor-ß signaling pathway. CONCLUSIONS: This study resulted in a set of exclusive and differentially expressed miRNAs in PDAC and CP can be assessed for their diagnostic value. In addition, studying the role of miRNA-target gene interactions in carcinogenesis may open new therapeutic avenues.

Synthesis, Characterization, Antiglycation Evaluation, Molecular Docking, and ADMET Studies of 4-Thiazolidinone Derivatives.

The design and development of new small-molecule glycation inhibitors are essential for preventing various chronic diseases, including diabetes mellitus, immunoinflammation, cardiovascular, and neurodegenerative diseases. 4-Thiazolidinone or thiazolidine-4-one is a well-known heterocyclic compound with the potential to inhibit the formation of advanced glycation end products. In the present work, we report the synthesis and characterization of four new 5-arylidene 3-cyclopropyl-2-(phenylimino)thiazolidin-4-one (1-4) compounds and their human serum albumin glycation inhibitory activity. One of the compounds 5-(2H-1,3-benzodioxol-5-ylmethylidene)-3-cyclopropyl-2-(phenylimino)-1,3-thiazolidin-4-one (3) showed potent inhibition in the synthesis of initial, intermediary, and final products of glycation reactions. Besides, conformational changes in the α-helix and ß-sheet (due to hyperglycemia) were also found to be reversed upon the addition of (3). Experimental findings were complemented by computational [molecular docking, ADME/Tox, and density functional theory (DFT)] studies. The docking scores of the compounds were in order 1 > 3 > 2 > 4, indicating the importance of the polar group at the 5-arylidene moiety. The results of ADME/Tox and DFT calculations revealed the safe nature of the compounds with high drug-likeness and stability. Overall, we speculate that the results of this study could provide valuable insights into the biological activity of 4-thiazolidinones.

Multi-omics approach for identifying CNV-associated lncRNA signatures with prognostic value in prostate cancer.

Background: Prostate cancer, the second most prevalent malignancy among men, poses a significant threat to affected patients' well-being due to its poor prognosis. Novel biomarkers are required to enhance clinical outcomes and tailor personalized treatments. Herein, we describe our research to explore the prognostic value of long non-coding RNAs (lncRNAs) deregulated by copy number variations (CNVs) in prostate cancer. Methods: The study employed an integrative multi-omics data analysis of the prostate cancer transcriptomic, CNV and methylation datasets to identify prognosis-related subtypes. Subtype-specific expression profiles of protein-coding genes (PCGs) and lncRNAs were determined. We analysed CNV patterns of lncRNAs across the genome to identify subtype-specific lncRNAs with CNV changes. LncRNAs exhibiting significant amplification or deletion and a positive correlation were designated CNV-deregulated lncRNAs. A prognostic risk score model was subsequently developed using these CNV-driven lncRNAs. Results: Six molecular subtypes of prostate cancer were identified, demonstrating significant differences in prognosis (P = 0.034). The CNV profiles of subtype-specific lncRNAs were examined, revealing their correlation with CNV amplification or deletion. Six lncRNAs (CCAT2, LINC01593, LINC00276, GACAT2, LINC00457, LINC01343) were selected based on significant CNV amplifications or deletions using a rigorous univariate Cox proportional risk regression model. A robust risk score model was developed, stratifying patients into high-risk and low-risk categories. Notably, our prognostic model based on these six lncRNAs exhibited exceptional predictive capabilities for recurrence-free survival (RFS) in prostate cancer patients (P = 0.024). Conclusions: Our study successfully identified a prognostic risk score model comprising six CNV-driven lncRNAs that could potentially be prognostic biomarkers for prostate cancer. These lncRNA signatures are closely associated with RFS, providing promising prospects for improved patient prognostication and personalized therapeutic strategies for novel prostate cancer treatment.

In vivo identification of angle dysgenesis and its relation to genetic markers associated with glaucoma using artificial intelligence.

PURPOSE: To predict the presence of angle dysgenesis on anterior-segment optical coherence tomography (ADoA) by using deep learning (DL) and to correlate ADoA with mutations in known glaucoma genes. PARTICIPANTS: In total, 800 high-definition anterior-segment optical coherence tomography (AS-OCT) images were included, of which 340 images were used to build the machine learning (ML) model. Images used to build the ML model included 170 scans of primary congenital glaucoma (16 patients), juvenile-onset open-angle glaucoma (62 patients), and adult-onset primary open-angle glaucoma eyes (37 patients); the rest were controls (n = 85). The genetic validation dataset consisted of another 393 images of patients with known mutations that were compared with 320 images of healthy controls. METHODS: ADoA was defined as the absence of Schlemm's canal, the presence of hyperreflectivity over the region of the trabecular meshwork, or a hyperreflective membrane. DL was used to classify a given AS-OCT image as either having angle dysgenesis or not. ADoA was then specifically looked for on AS-OCT images of patients with mutations in the known genes for glaucoma. RESULTS: The final prediction, which was a consensus-based outcome from the three optimized DL models, had an accuracy of >95%, a specificity of >97%, and a sensitivity of >96% in detecting ADoA in the internal test dataset. Among the patients with known gene mutations, ( MYOC, CYP1B1, FOXC1, and LTBP2 ) ADoA was observed among all the patients in the majority of the images, compared to only 5% of the healthy controls. CONCLUSION: ADoA can be objectively identified using models built with DL.

In silico evaluation of 4-thiazolidinone-based inhibitors against the receptor for advanced glycation end products (RAGE).

Non-enzymatic glycation of biomolecules by reducing sugars led to several products, including the advanced glycation end products (AGEs), the accumulation of which has been linked to various life-threatening diseases. The binding of AGEs to their respective protein receptors for advanced glycation end products (RAGE) can initiate a cascade of reactions, which may alter physiological conditions. The present work investigates the potential of 4-thiazolidinones as RAGE inhibitors. We performed an extensive computational study to identify the structural requirements needed to act as RAGE inhibitors. To achieve this goal, 4-thiazolidinone-based compounds available in PubChem, ZINC15, ChEMBL, and ChEBI databases were screened against RAGE (PDB: 4LP5), leading to the identification of top five drug-like candidates with a high binding affinity to RAGE V-domain catalytic region. Drug likeness, absorption, distribution, metabolism, excretion, and toxicity (ADMET) of the top-scoring compounds have been studied and discussed. Global molecular descriptors, chemical reactivity, hardness, softness, etc., have been estimated. Finally, molecular dynamics (MD) simulations at 100 ns were carried out to check the stability and other properties. Overall, we believe that the identified compounds can potentially attenuate RAGE-AGE interactions.Communicated by Ramaswamy H. Sarma.

Exploring the electronic structure, mechanical behaviour, thermal and high-temperature thermoelectric response of CoZrSi and CoZrGe Heusler alloys.

By using density functional theory, we have explored the structural, electro-mechanical, thermophysical and thermoelectric properties of CoZrSi and CoZrGe Heusler alloys. The ground state stability was determined by optimising the energy in various configurations like type I, II, and III. It was found that these alloys stabilized in the ferromagnetic phase in type I. We employed the Generalised Gradient Approximation and modified Becke-Johnson potentials to explore the electronic structure. The band structures of each of these Heusler alloys exhibit a half-metallic nature. Additionally, the computed second-order elastic parameters reveal their ductile nature of them. To understand the stability of the alloys at different pressures and temperatures, we investigated various thermodynamic parameters using the Quasi-Harmonic Debye model. We obtained the transport coefficients using the Boltzmann theory. Our findings indicate that these alloys can be used in spintronics and thermoelectric domains.