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To find potential inhibitors of Naegleria fowleri S-adenosyl-L-homocysteine hydrolase (NfSAHH), a brain-eating parasite, structure-based drug design was adopted. N. fowlerica causes primary amebic meningoencephalitis (PAM), a fatal central nervous system (CNS) disorder if untreated. NfSAHH protein (PDB ID: 5v96), involved in parasite growth and gene regulation, was targeted and screened against 163 metabolites from Gossypium hirsutum (cotton plant). With the aid of different software and web tools, the metabolites were subjected to several analyses. The RMSD was evaluated to validate our molecular docking strategy. Neplanocin A, a common anti-parasitic medication, was used as a reference to select top ligands for post-docking studies. Significant interactions were observed with residues THR-198, HIS-395, and MET-400. The drug-likeness of the top fifty hits was analyzed using Lipinski, Ghose, Veber, Egan, and Muegge rules. The top ten compounds following Lipinski’s RO5 were studied regarding medicinal chemistry, pharmacokinetic simulation, and Swiss target prediction. Advanced strategies, including molecular dynamic simulations, binding energy calculations, and principal component analysis, were employed for the top three hits, namely curcumin, heliocide H2, and piceid, which indicated that heliocide H2 is the most promising candidate, while curcumin and piceid may need further optimization to improve their stability. Overall, the top ten phytochemicals, dotriacontanol, melissic acid, curcumin, 6,6′-dimethoxygossypol, phytosphingosine 2, methyl stearate, stearic acid, piceid, heliocide H2, and 6-methoxygossypol, reported in our study, are worthy enough to be subjected to in vivo and in vitro experimentation to find a novel drug to treat PAM.

PARP inhibitors (PARPi) have drastically changed the treatment landscape of advanced ovarian tumors with BRCA mutations. However, the impact of this class of inhibitors in patients with advanced BRCA -mutant breast cancer is relatively modest. Using a syngeneic genetically-engineered mouse model of breast tumor driven by Brca1 deficiency, we show that tumor-associated macrophages (TAMs) blunt PARPi efficacy both in vivo and in vitro. Mechanistically, BRCA1-deficient breast tumor cells induce pro-tumor polarization of TAMs, which in turn suppress PARPi-elicited DNA damage in tumor cells, leading to reduced production of dsDNA fragments and synthetic lethality, hence impairing STING-dependent anti-tumor immunity. STING agonists reprogram M2-like pro-tumor macrophages into an M1-like anti-tumor state in a macrophage STING-dependent manner. Systemic administration of a STING agonist breaches multiple layers of tumor cell-mediated suppression of immune cells, and synergizes with PARPi to suppress tumor growth. The therapeutic benefits of this combination require host STING and are mediated by a type I IFN response and CD8 + T cells, but do not rely on tumor cell-intrinsic STING. Our data illustrate the importance of targeting innate immune suppression to facilitate PARPi-mediated engagement of anti-tumor immunity in breast cancer. PARP inhibitor (PARPi) therapy has demonstrated only modest efficacy in advanced breast cancer with BRCA mutations. Here the authors show that, by suppressing PARPi-triggered DNA damage and reducing dsDNA production in BRCA1-deficient breast tumor cells, tumor associated macrophages contribute to PARPi resistance, that can be overcome by STING agonism.

Cotton ( Gossypium hirsutum L.), a crucial global fibre and oil seed crop faces diverse biotic and abiotic stresses. Among these, temperature stress strongly influences its growth, prompting adaptive physiological, biochemical, and molecular changes. In this study, we explored the proteomic changes underscoring the heat stress tolerance in the leaves of two locally developed cotton genotypes, i.e., heat tolerant (GH-Hamaliya H tol ) and heat susceptible (CIM-789 H sus ), guided by morpho-physiological and biochemical analysis. These genotypes were sown at two different temperatures, control (35°C) and stress (45°C), in a glasshouse, in a randomized complete block design (RCBD) in three replications. At the flowering stage, a label-free quantitative shotgun proteomics of cotton leaves revealed the differential expression of 701 and 1270 proteins in the tolerant and susceptible genotypes compared to the control, respectively. Physiological and biochemical analysis showed that the heat-tolerant genotype responded uniquely to stress by maintaining the net photosynthetic rate ( Pn ) (25.2–17.5 μmolCO 2 m -2 S -1 ), chlorophyll (8.5–7.8mg/g FW), and proline contents (4.9–7.4 μmole/g) compared to control, supported by the upregulation of many proteins involved in several pathways, including photosynthesis, oxidoreductase activity, response to stresses, translation, transporter activities, as well as protein and carbohydrate metabolic processes. In contrast, the distinctive pattern of protein downregulation involved in stress response, oxidoreductase activity, and carbohydrate metabolism was observed in susceptible plants. To the best of our knowledge, this is the first proteomic study on cotton leaves that has identified more than 8000 proteins with an array of differentially expressed proteins responsive to the heat treatment that could serve as potential markers in the breeding programs after further experimentation.

COVID-19 is currently considered the ninth-deadliest pandemic, spreading through direct or indirect contact with infected individuals. It has imposed a consistent strain on both the financial and healthcare resources of many countries. To address this challenge, there is a pressing need for the development of new potential therapeutic agents for the treatment of this disease. To identify potential antiviral agents as novel dual inhibitors of SARS-CoV-2, we retrieved 404 alkaloids from 12 selected medicinal antiviral plants and virtually screened them against the renowned catalytic sites and favorable interacting residues of two essential proteins of SARS-CoV-2, namely, the main protease and spike glycoprotein. Based on docking scores, 12 metabolites with dual inhibitory potential were subjected to drug-likeness, bioactivity scores, and drug-like ability analyses. These analyses included the ligand–receptor stability and interactions at the potential active sites of target proteins, which were analyzed and confirmed through molecular dynamic simulations of the three lead metabolites. We also conducted a detailed binding free energy analysis of pivotal SARS-CoV-2 protein inhibitors using molecular mechanics techniques to reveal their interaction dynamics and stability. Overall, our results demonstrated that 12 alkaloids, namely, adouetine Y, evodiamide C, ergosine, hayatinine, (+)-homoaromoline, isatithioetherin C, N,alpha-L-rhamnopyranosyl vincosamide, pelosine, reserpine, toddalidimerine, toddayanis, and zanthocadinanine, are shortlisted as metabolites based on their interactions with target proteins. All 12 lead metabolites exhibited a higher unbound fraction and therefore greater distribution compared with the standards. Particularly, adouetine Y demonstrated high docking scores but exhibited a nonspontaneous binding profile. In contrast, ergosine and evodiamide C showed favorable binding interactions and superior stability in molecular dynamics simulations. Ergosine demonstrated exceptional performance in several key pharmaceutical metrics. Pharmacokinetic evaluations revealed that ergosine exhibited pronounced bioactivity, good absorption, and optimal bioavailability. Additionally, it was predicted not to cause skin sensitivity and was found to be non-hepatotoxic. Importantly, ergosine and evodiamide C emerged as superior drug candidates for dual inhibition of SARS-CoV-2 due to their strong binding affinity and drug-like ability, comparable to known inhibitors like N3 and molnupiravir. This study is limited by its in silico nature and demands the need for future in vitro and in vivo studies to confirm these findings.

Schizophrenia is a complex psychiatric disorder marked by delusions, memory impairments, hallucinations, disorganized behavior, and severe cognitive deficits. Targeting the dopamine transporter (DAT) protein is promising for treating cognitive symptoms, especially in patients resistant to antipsychotic treatments. In this study, phytochemicals from six medicinal plants underwent virtual screening, and molecular dynamics simulation to identify potential agents targeting DAT. Key drug-like properties, safety, and biological activity were evaluated for identified hits. Pharmacokinetic simulation and pharmacophoric analysis were also performed. Among 990 screened phytochemicals, three alkaloids and six amides, predominantly from Piper retrofractum, and one diterpene were identified as potential antischizophrenic agents based on their stronger binding affinities and favorable docking scores compared to the standard (Lumateperone). Amides showed more potential for DAT than alkaloids. The dynamic behavior and stability of the top three amides, namely, Chenoalbicin, Dipiperamide G, and Lyciumamide C, were evaluated using various molecular dynamics analyses. RMSD (Root Mean Square Deviation), RMSF (Root Mean Square Fluctuation), Rg (Radius of Gyration), and SASA (Solvent Accessible Surface Area) analyses demonstrated favorable characteristics for all three ligands. However, binding free energy, cross-correlation, PCA (Principal Component Analysis) and FEL (Free Energy Landscape) analyses indicated that Lyciumamide C exhibited the highest stability and binding affinity in dynamic environments, Pharmacophoric features highlighted the distinct interacting components for the top three amides. Pharmacokinetic simulations revealed significant peak concentrations and sustained levels can be indicated as Lyciumamide C > Chenoalbicin > Dipiperamide G. The higher and more sustained brain concentrations of Lyciumamide C suggest its most promising pharmacokinetic profile for targeting DAT. Overall, our screened metabolites followed drug-ability criteria and require further experimental validation.

Although the decisions of policy professionals are often more consequential than those of individuals in their private capacity, there is a dearth of studies on the biases of policy professionals: those who prepare and implement policy on behalf of elected politicians. Experiments conducted on a novel subject pool of development policy professionals (public servants of the World Bank and the Department for International Development in the UK) show that policy professionals are indeed subject to decision-making traps, including the effects of framing outcomes as losses or gains, and, most strikingly, confirmation bias driven by ideological predisposition, despite having an explicit mission to promote evidence-informed and impartial decision making. These findings should worry policy professionals and their principals in governments and large organizations, as well as citizens themselves. A further experiment, in which policy professionals engage in discussion, shows that deliberation may be able to mitigate the effects of some of these biases.

The emergence of multi-modal models such as Apple’s MM1 signifies a transition towards on-device artificial intelligence, diminishing dependence on cloud inference. However, quantum developments render classical cryptography vulnerable to data breach. We present QSAFE-MM1, a quantum-resilient security architecture that incorporates Federated Learning (FL), Fully Homomorphic Encryption (FHE), and lattice-based cryptography to enhance MM1’s security. Federated Learning (FL) facilitates decentralised training without the transmission of raw data, so safeguarding user privacy and attaining 94% processing efficiency, 1020 J energy consumption, 7% per hour battery depletion, and a thermal increase of + 4 °C. Fully Homomorphic Encryption (FHE) facilitates encrypted inference, preventing data breaches while processing; yet, it results in an 81% efficiency reduction, consumes 1600 J, causes a 13% per hour energy drain, and increases temperature by 7 °C. The complete QSAFE-MM1 stack (FL + FHE + DP) achieves 79% efficiency, with 1700 J, 14%/hr, and + 8 °C, indicating secure-performance trade-offs. Quantum resistance is attained by NIST-compliant lattice-based methods that are impervious to Shor’s algorithm, and asymmetric masking eliminates personally identifiable information during training. Empirical assessment verifies that QSAFE-MM1 maintains model accuracy (± 1.2% variance) and latency (< 9% overhead) while ensuring post-quantum security. QSAFE-MM1 establishes a new standard for mobile AI security, harmonising quantum safety, user privacy, and performance under strict resource limitations, thereby presenting MM1 as a frontrunner in secure, on-device intelligence.

Maize (Zea mays L.) is a globally significant cereal crop, but its productivity is often constrained by weed competition, particularly from jungle rice (Echinochloa colona L.). This study examines the impact of varying densities of jungle rice on maize growth, yield, and quality to identify sustainable weed management strategies. The experiment was conducted at Gomal University, Pakistan, and employed a randomized complete block design with six jungle rice density treatments (0, 5, 10, 15, 20, and 25 plants/m²). The results revealed that increasing the jungle rice density significantly reduced the maize plant height, grain yield, 1000-grain weight, and leaf area index, whereas lower densities (≤10 plants m-²) had minimal effects. At the highest density (25 plants/m²), the maize yield and grain protein content were reduced by 59.68% and 13.05%, respectively, compared with those of the control. These findings underscore the competitive threat posed by jungle rice and highlight the necessity of maintaining weed densities below 10 plants m-² to optimize maize productivity and resource use efficiency. This study provides actionable insights for integrated weed management, contributing to sustainable agricultural practices and enhanced profitability in maize-based systems.

This study investigates fluid flow and convective heat transfer within a smooth, two-dimensional T-shaped junction using a numerical approach. Simulations were conducted by varying the volumetric flow rate ratio r (0.25, 0.5, 0.75, and 1), the Reynolds number Re (500 to 2500), the Prandtl number Pr (1), and the cross-sectional width ratio w (0.5 to 2.5) of the outlet. The fluid dynamics were solved using the vorticity–stream function formulation with a compact upwind finite difference scheme and the Implicit-Explicit (IMEX) method, implemented in MATLAB. Flow behavior was analyzed through streamline and isotherm contours, while local and average Nusselt numbers were computed along the junction walls. The results show that lower r values lead to stronger vortex formation and asymmetry in the flow and temperature fields, while r  = 1 yields symmetric and stable patterns. Increasing Re enhances heat transfer and transitions the flow toward unsteady regimes. Similarly, wider outlet configurations (higher w ) promote recirculation and thermal mixing. This study provides valuable insights into how inlet flow, outlet shape, and fluid characteristics interact to influence heat transfer and flow behavior in a smooth T-shaped junction. It also provides insights that can help improve the design of heat exchangers, microfluidic systems, and industrial piping.

Approximately 50% of patients with metastatic HER2-positive (HER2+) breast cancer develop brain metastases (BCBMs). We report that the tumor suppressor p16 INK4A is deficient in the majority of HER2+ BCBMs. p16 INK4A -deficiency as measured by protein immunohistochemistry predicted response to combined tucatinib and abemaciclib in orthotopic patient-derived xenografts (PDXs) of HER2 + BCBMs. Our findings establish the rationale for a biomarker-driven clinical trial of combined CDK4/6- and HER2-targeted agents for patients with HER2 + BCBM. HER2+ breast cancer often develop brain metastases (BCBMs) that are difficult to treat. Here, the authors show that p16 INK4A loss in BCBMs from HER2+ breast tumors results in resistance to the HER2 inhibitor Tucatinib, and that CDK4/6 inhibition can restore sensitivity to this drug.