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.

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.

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.

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.

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.

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.

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.

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 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.

Holistic Exploration of Structural, Electronic, Magnetic, Transport, Mechanical, and Thermodynamic Characteristics, Including Curie Temperature Analysis.

Through intricate calculations, the density functional theory (DFT) implemented in the Wien2k code was employed to comprehensively investigate a wide range of material characteristics. Our study encompasses an exhaustive analysis of structural stability, electronic properties, magnetic behaviors, transport phenomena, mechanical responses, and thermodynamic profiles of two notable instances of filled Skutterudites, namely, CeNi4P12 and DyCo4Sb12, which have been thoroughly explored. These computations were performed using the WIEN 2K code, combining local orbitals and the full-potential linearized augmented plane-wave approach. The findings provided insight into the wide range of properties of these materials. In this methodology, the exchange-correlation potential relies on the local-density approximation. We conducted the calculations with and without incorporating spin-orbit interactions. The results obtained provide information about the lattice constant, bulk modulus, and pressure derivative. The stability, as indicated by the P-V graphical plot, suggests that there are no structural phase transitions from the cubic symmetry structure. Notably, our work includes an examination of Curie temperatures, which are pivotal in understanding magnetic phase transitions. The validated elastic properties further support the material's stability and corroborate its ductile nature. These alloys should be considered for spintronic and thermoelectric applications due to their estimated transport characteristics and the observed ductile nature. To enhance our understanding of the thermal stability of antimony-based compounds, we have made reliable estimations of the thermophysical characteristics. By integrating theoretical insights with practical implications, we bridge the gap between fundamental understanding and material design applications. Using DFT in the Wien2k framework, we discover connections and patterns among different properties, showing how to create materials with specific functions and better performance. This approach not only advances our fundamental comprehension of materials but also promises innovation across various technological domains.

Understanding the computational insights of spin-polarised density functional theory into the newly half-metallic f electron-based actinide perovskites SrMO3 (M = Pa, Np, Cm, Bk).

Here, we investigated the structural, mechanical, electronic, magnetic, thermodynamic and thermoelectric properties of Strontium based simple perovskites SrMO3 (M = Pa, Np, Cm, Bk) by using density functional theory. First and foremost, the ground state stability of these perovskites was initially evaluated by optimizing their total ground state energies in distinct ferromagnetic and non-magnetic configurations. The structural stability in terms of their ground state energies defines that these alloys stabilize in ferromagnetic rather than competing non-magnetic phase. From the understandings of mechanical parameters these alloys are characterized to be ductile in nature. After that, two approximation schemes namely Generalized Gradient approximation and Tran-Blaha modified Becke-Johnson potential have been used to find their intimate electronic structures which displays the half-metallic nature of these alloys. Further, we have verified temperature and pressure effect on these alloys. Finally, the transport properties have been evaluated within the selected temperature range of 150-900 K. In view of this, the different transport parameters along with half-metallic nature advocate their possible applications in thermoelectric and spintronics devices.

Correction: Theoretical exploration of inherent electronic, structural, mechanical, thermoelectric, and thermophysical response of KRu4Z12 (Z = As12, Sb12) filled skutterudite materials.

[This corrects the article DOI: 10.1039/D3RA05546A.].

VirulentPred 2.0: An improved method for prediction of virulent proteins in bacterial pathogens.

Virulence proteins in pathogens are essential for causing disease in a host. They enable the pathogen to invade, survive and multiply within the host, thus enhancing its potential to cause disease while also causing evasion of host defense mechanisms. Identifying these factors, especially potential vaccine candidates or drug targets, is critical for vaccine or drug development research. In this context, we present an improved version of VirulentPred 1.0 for rapidly identifying virulent proteins. The VirulentPred 2.0 is based on training machine learning models with experimentally validated virulent protein sequences. VirulentPred 2.0 achieved 84.71% accuracy with the validation dataset and 85.18% on an independent test dataset. The models are trained and evaluated with the latest sequence datasets of virulent proteins, which are three times greater in number than the proteins used in the earlier version of VirulentPred. Moreover, a significant improvement of 11% in the prediction accuracy over the earlier version is achieved with the best position-specific scoring matrix (PSSM)-based model for the latest test dataset. VirulentPred 2.0 is available as a user-friendly web interface at https://bioinfo.icgeb.res.in/virulent2/ and a standalone application suitable for bulk predictions. With higher efficiency and availability as a standalone tool, VirulentPred 2.0 holds immense potential for high throughput yet efficient identification of virulent proteins in bacterial pathogens.

Tailoring the intrinsic magneto-electronic, mechanical, thermo-physical and thermoelectric response of cobalt-based Heusler alloys: an ab initio insight.

We conducted a comprehensive analysis of the fundamental properties of CoHfSi and CoHfGe half-Heusler alloys using density functional theory simulations implemented in Wien2k. To begin, structural optimization revealed that both alloys effectively adopt a cubic C1b structure, with Y1 as the dominant ferromagnetic phase. Electronic properties were computed using various approximation schemes, including the Generalized Gradient Approximation and the modified Becke-Johnson potential. The examination of electronic band structures and their accompanying density of states using the modified Becke-Johnson functional approach unveiled their half-metallic nature. In this context, the spin-up channel exhibited semiconductor behaviour, while the spin-down channel displayed metallic characteristics. Additionally, the spin-splitting observed in their resulting band structures contributed to a net magnetism within their lattice structure, making them promising candidates for spintronic applications. We also scrutinized Seebeck coefficients, electrical conductivity, thermal conductivity, and power factor to gain a better understanding of their thermoelectric properties.

Theoretical exploration of inherent electronic, structural, mechanical, thermoelectric, and thermophysical response of KRu4Z12 (Z = As12, Sb12) filled skutterudite materials.

Using the density functional theory methodology, we have thoroughly examined KRu4As12 and KRu4Sb12 skutterudites, including their structural, electronic, mechanical, transport, and thermodynamic properties. First and foremost, using the Birch-Murnaghan equation of state, the structural stability has been calculated in terms of their total ground state and cohesive energies. With the use of the approximation approaches GGA and GGA + mBJ, the electrical structure and density of the states reveal their metallic nature. This demonstration predicts the dominant ferromagnetic spin configuration of materials by considering their electronic behavior and magnetic interactions. The ductile behavior of these alloys is also addressed by their mechanical qualities, which indicate how they might be used in engineering and industrial settings. Moreover, the semi-classical Boltzmann transport theory has been employed to examine the Seebeck coefficient as well as the electric and thermal conductivities. The general tendency of these compounds demonstrates their various potential uses as electrode materials. The quasi-harmonic Debye approximation is a method used to analyze the stability of a system under high pressures and accounts for the temperature dependency of thermodynamics. It combines the quasi-harmonic approximation, which considers the anharmonicity of vibrations, with the Debye model, which describes the vibrational modes of a solid. This approach allows for a more accurate representation of the system's behavior at different temperatures and pressures. By implementing this approximation, researchers can gain insights into the stability and thermodynamic properties of materials under extreme conditions.

In silico studies of alkaloids and their derivatives against N-acetyltransferase EIS protein from Mycobacterium tuberculosis.

Antibiotic resistance against Mycobacterium tuberculosis (M.tb.) has been a significant cause of death worldwide. The Enhanced intracellular survival (EIS) protein of the bacteria is an acetyltransferase that multiacetylates aminoglycoside antibiotics, preventing them from binding to the bacterial ribosome. To overcome the EIS-mediated antibiotics resistance of M.tb., we compiled 888 alkaloids and derivatives from five different databases and virtually screened them against the EIS receptor. The compound library was filtered down to 87 compounds, which underwent additional analysis and filtration. Moreover, the top 15 most prominent phytocompounds were obtained after the drug-likeness prediction and ADMET screening. Out of 15, nine compounds confirmed the maximum number of hydrogen bond interactions and reliable binding energies during molecular docking. Additionally, the Molecular dynamics (MD) simulation of nine compounds showed the three most stable complexes, further verified by re-docking with mutated protein. The density functional theory (DFT) calculation was performed to identify the HOMO-LUMO energy gaps of the selected three potential compounds. Finally, our selected top lead compounds i.e., Alkaloid AQC2 (PubChem85634496), Nobilisitine A (ChEbi68116), and N-methylcheilanthifoline (ChEbi140673) demonstrated more favourable outcomes when compared with reference compounds (i.e., 39b and 2i) in all parameters used in this study. Therefore, we anticipate that our findings will help to explore and develop natural compound therapy against multi and extensively drug-resistant strains of M.tb.Communicated by Ramaswamy H. Sarma.

Machine learning analysis of lung squamous cell carcinoma gene expression datasets reveals novel prognostic signatures.

BACKGROUND: Lung squamous cell carcinoma (LUSC) patients are often diagnosed at an advanced stage and have poor prognoses. Thus, identifying novel biomarkers for the LUSC is of utmost importance. METHODS: Multiple datasets from the NCBI-GEO repository were obtained and merged to construct the complete dataset. We also constructed a subset from this complete dataset with only known cancer driver genes. Further, machine learning classifiers were employed to obtain the best features from both datasets. Simultaneously, we perform differential gene expression analysis. Furthermore, survival and enrichment analyses were performed. RESULTS: The kNN classifier performed comparatively better on the complete and driver datasets' top 40 and 50 gene features, respectively. Out of these 90 gene features, 35 were found to be differentially regulated. Lasso-penalized Cox regression further reduced the number of genes to eight. The median risk score of these eight genes significantly stratified the patients, and low-risk patients have significantly better overall survival. We validated the robust performance of these eight genes on the TCGA dataset. Pathway enrichment analysis identified that these genes are associated with cell cycle, cell proliferation, and migration. CONCLUSION: This study demonstrates that an integrated approach involving machine learning and system biology may effectively identify novel biomarkers for LUSC.

Exploring the half-metallic ferromagnetism, dynamical and mechanical stability, optoelectronic and thermoelectric properties of K2NaMI6 (M = Mn, Co, Ni) for spintronic applications.

The structural stability, optoelectronic and magnetic characteristics of K2NaMI6 (M = Mn, Co, and Ni) halide double perovskites have been demonstrated to be explained using density functional theory computations. The prominent generalized gradient approximation and integration of the mBJ potential are implemented to estimate the exchange-correlation potential, which is the only unidentified parameter in the state-of-the-art formulism. The structural optimization, mechanical stability criteria, and tolerance factor demonstrate the reliability of the double perovskites in a cubic structure with Fm3m symmetry. The elastic constants facilitated mechanical stability and revealed the brittle nature of these double perovskites. The spin-polarized electronic band profile and the behaviour of the dielectric constant and absorption coefficient in the spin-up and down channels show the presence of half-metallic nature in these materials. Additionally, we examined magnetism and the genesis of the half-metallic gap in this article. The half-metallic and magnetic properties are attributed to the unpaired electrons in the split d-orbitals of the M-sited elements in the crystal field. The Mn-, Co-, and Ni-based double perovskites were found to possess total magnetic moments of 4 µB, 4 µB, and 1 µB, respectively, with the transition metal atoms comprising up the majority of this magnetic moment. The Fermi level's perfect spin polarisation promotes the potential application of double perovskites in spintronic technology.

Probing the structural, mechanical, phonon, thermal, and transport properties of magnetic halide perovskites XTiBr3 (X = Rb, Cs) through ab-initio results.

Herein, we have first reported the intrinsic properties, including structural, mechanical, electronic, magnetic, thermal, and transport properties of XTiBr3 (X = Rb, Cs) halide perovskites within the simulation scheme of density functional theory as integrated into Wien2k. First and foremost, the structural stability in terms of their ground state energies has been keenly evaluated from their corresponding structural optimizations, which advocate that XTiBr3 (X = Rb, Cs) has a stable ferromagnetic rather than the competing non-magnetic phase. Later on, the electronic properties have been computed within the mix of two applied potential schemes like Generalized Gradient Approximation (GGA) along with Trans-Bhala modified Becke Johnson (TB-mBJ), which thoroughly addresses the half-metallic behaviour with spin-up as metallic and in contrast to opposite spin-down channel signatures the semiconducting behaviour. Furthermore, the spin-splitting seen from their corresponding spin-polarised band structures offers a net magnetism of 2 µB which lends their opportunities to unlock the application branch of spintronics. In addition, these alloys have been characterised to show their mechanical stability describing the ductile feature. Moreover, phonon dispersions decisively certify the dynamical stability within the density functional perturbation theory (DFPT) context. Finally, the transport and thermal properties predicted within their specified packages have also been forwarded in this report.