Sourav Bhattacharjee

The mucus layer, a physiologically relevant hydrogel found in the human gut, respiratory and genital tracts, and on ocular surfaces, forms a formidable physiological barrier with mucin proteins, creating its hygroscopic mesh that restricts the permeation of nanomedicines. Hydrophilic ligands such as polyethylene glycol, when grafted onto nanoparticles, are known to enhance mucopermeation, although the mechanisms remain unclear. To investigate this, 79 ligands were virtually selected from the ChEMBL and DrugBank databases based on their similarity to triethylene glycol (TEG), an analog of polyethylene glycol, and on Lipinski–Veber oral bioavailability parameters. The ligands were ranked by similarity score (SS), proposed here as a structural bioinformatic parameter for automated ligand-candidate prioritization. A final set of 15 ligands was selected and divided into terciles. The top tercile, which included triethylene glycol, had the highest SS values. These 15 ligands were docked with the equilibrated (unglycosylated) MUC5AC protein structure using the PatchDock and AutoDock Vina programs. For each ligand, the average docking or binding score (depending on the program used) and the average atomic contact energy (mean of the top five atomic contact energies) were calculated. The SS values correlated strongly with the average atomic contact energies and moderately with the average docking and binding scores, as well as with the Lipinski–Veber parameters. These trends were confirmed using 15 additional ligands docked on unglycosylated mucin, with the two top- and two bottom-ranked ligands and TEG further evaluated on glycosylated MUC5AC using PatchDock. Overall, the SS and the average atomic contact energy emerged as effective indicators for efficient and accurate surface-ligand screening.

Intracellular mitochondria are fascinating organelles that began their evolutionary journey as symbiotic α‐proteobacteria, negotiating safety and residence in (eukaryotic) cells in exchange for managing the host's energy demands. The archaic bacterium that was engulfed by cells has left its mark on modern mitochondria, including the well‐studied mitochondrial membrane, matrix, and genetic makeup. Similarly, nanoparticles, abundant in the atmosphere and inherently reactive, have interacted with life forms throughout their existence. One such multidimensional, complex reciprocity is the mitochondria‐nanoparticle interaction, which is biomedically relevant. Charged nanoparticles may disrupt the electron transport chain in the mitochondrial membrane that produces ATP, thereby depleting cells of their energy supply. Additionally, a disrupted electron transport produces excess reactive oxygen species, leading to oxidative stress, known to cause neurodegenerative, cardiovascular, and neurological diseases. However, recent advancements in nanotechnology and synthesis have uncovered novel opportunities to exploit this mitochondria‐nanoparticle interface, including theranostic possibilities via mitochondrial targeting with functionalized nanoparticles, with or without an encapsulated payload of theranostic agents such as peptides and genetic materials. This account will strive to understand the intricacies of this interface and elucidate the theranostic possibilities it offers.

The mucus layer — a physiologically relevant hydrogel found in the human gut, respiratory and genital tracts, and on ocular surfaces — forms a formidable, highly evolved barrier, with mucin proteins creating its hygroscopic mesh. This barrier restricts the passage of nanomedicines, which tend to adhere to it and undergo recycling. Hydrophilic ligands such as polyethylene glycol, when grafted onto nanoparticles, are known to enhance mucopermeation, although the underlying mechanisms remain unclear. To investigate this, 79 ligands were virtually selected from the ChEMBL and DrugBank databases based on their similarity to triethylene glycol — an analogue of polyethylene glycol — and on Lipinski–Veber oral bioavailability parameters. The ligands were ranked by similarity score (SS), proposed here as a structural bioinformatic parameter for automated ligand‑candidate prioritisation. A final set of 15 ligands was selected — five ligands per tercile. The top tercile, which included triethylene glycol, had the highest SS values. These 15 ligands were docked with the equilibrated MUC5AC protein structure using two different docking programs. For each ligand, the average docking or binding score (depending on the program used) and the average atomic contact energy (mean of the top five atomic contact energies) were calculated. The SS values showed strong correlation with the average atomic contact energies and moderate correlation with both the average docking and binding scores, and with the six Lipinski–Veber parameters. Overall, the SS values and the average atomic contact energy emerged as useful indicators, highlighting their potential value for efficient and accurate surface‑ligand screening in future nanomedical applications.

Insulin, a therapeutic peptide for Type I diabetes, can interact with polymers commonly found in healthcare settings via hydrophobic interactions, potentially triggering agglomeration while reducing its bioavailability and therapeutic efficacy.

Background and Aims: Computed tomography texture analysis, powered by machine learning techniques, may differentiate clear cell renal cell carcinoma (ccRCC) from other renal tumor subtypes, such as papillary and chromophobe variants, or benign renal masses such as oncocytomas, as demonstrated here using the KiTS23 dataset. Methods: After excluding multifocal cases to avoid lesion‐level labeling ambiguity and intrapatient lesion heterogeneity, 396 cases were included. Using PyRadiomics, 386 radiomics features were initially extracted from preprocessed computed tomography volumes and tumor segmentation masks. Subsequent multistage feature selection reduced the candidate feature set to 73 radiomics descriptors, and LASSO further selected six features for the primary classifier. Candidate machine‐learning models were evaluated, with class‐weighted Logistic Regression selected as the primary model. Threshold adjustment using the Youden index and bootstrap feature‐selection stability analysis were performed. Results: The LASSO‐selected class‐weighted Logistic Regression model showed the best overall performance. Using the default probability threshold of 0.5, it achieved an accuracy of 76.5%, an area under the receiver operating characteristic curve of 80.4%, a precision of 91.2%, a sensitivity of 73.8%, and a specificity of 82.9% on the held‐out test set. Youden‐index thresholding increased sensitivity but reduced specificity; therefore, the fixed threshold of 0.5 was retained as the primary operating threshold. Bootstrap stability analysis showed that several LASSO‐selected radiomics descriptors were repeatedly selected across resampled training cohorts. Conclusions: Radiomics‐based machine learning shows promise for discriminating ccRCC from non‐ccRCC renal tumors, but the results remain preliminary and have been evaluated internally. Given the moderate discrimination and lack of external validation, further studies using larger, multicenter datasets with standardized radiomics workflows are necessary.

An in vitro study was conducted to investigate the cellular uptake and compartmentalization of internalized insulin in human hepatocellular carcinoma-derived HepG2 cells using confocal laser scanning microscopy. The cellular nuclei and actin filaments were stained with Hoechst (λ_ex = 405 nm, λ_em = 415–485 nm) and Alexa Phalloidin 647 (λ_ex = 649 nm, λ_em = 658–775 nm) dyes, respectively. Besides, recombinant human insulin, labeled with fluorescein isothiocyanate (λ_ex = 491 nm, λ_em = 502–600 nm), was used for the uptake studies (37 °C) at a concentration of 0.5 mg mL⁻¹, with timepoints at 15 min and 30 min. The optimized tri-staining protocol proved adequate for confocal microscopy, while the 2D and 3D renditions revealed insulin uptake by HepG2 cells. The internalized (fluorescent) insulin was taken up into vesicles (receptor-mediated endocytosis) and spread throughout the cytoplasm, with little or no interaction with the actin network. Some of the internalized insulin accessed the cellular nuclei. Such nuclear interaction might explain insulin's role in influencing translation, cellular proliferation, and (overall) growth. The study developed an optimized tri-staining procedure that enabled in-depth visualization of HepG2 cells (actin network and nuclei) with internalized insulin, providing a better understanding of insulin's cellular uptake and nuclear interaction.

Objectives: To understand the complexity of disease pathology through the prism of septic arthritis, especially the reliability of popular and, yet, arbitrary thresholds like synovial leucocyte counts of ≥ 100,000/μL suggestive of it, with the help of statistical analysis and logistic regression.Methods: An anonymized patient dataset comprising 360 swollen joint episodes was collated along with a range of patient attributes, including age, gender, comorbidity (e.g., diabetes, gout, pseudogout, immunosuppression), prior administration of antibiotics and washout of the affected joint, isolation of crystals from synovial aspirate, blood/synovial fluid culture growth, and synovial aspirate cell count. The dataset was subjected to statistical analysis (e.g., sensitivity, specificity, predictive and likelihood ratios) and logistic regression modeling, with results compared to the synovial leucocyte count thresholds of ≥ 100,000/μL and ≥ 50,000/μL.Results: The logistic regression model (sensitivity 50%, specificity 97.04%) outperformed the models based on arbitrary thresholds like a synovial leucocyte count of ≥ 100,000/μL (sensitivity 48.21%, specificity 88.16%) or ≥ 50,000/μL (sensitivity 64.29%, specificity 69.74%) in predicting septic arthritis. Independent variables like age, presence of gout, and autoimmune arthritis as comorbidities, hip joint involvement, synovial aspirate leucocyte count, and crystals in aspirated fluid demonstrated a significant (p < 0.05) correlation to septic arthritis.Conclusion: Septic arthritis presents a multivariate correlation that deserves a holistic oversight rather than singling out individual factors. Data mining platforms like logistic regression can investigate the complex interplay among these individual variables while making a diagnosis not only in septic arthritis but also in other diseases with multisystem involvement, infective or non‐infective alike.

As the scientific community celebrates the centenary year of insulin discovery, developing oral insulin delivery systems remains challenging, with disappointing progress. On the contrary, injectable formulations, despite initial setbacks due to poor patient compliance and issues with painful daily injections, have improved considerably—and currently remain the mainstay of insulin therapeutics. Advanced microneedle technology has enabled insulin delivery with minimal nociception in conjunction with automated, user‐friendly delivery platforms. Furthermore, integrating modalities like insulin pumps delivering precise doses based on blood glucose monitoring has emerged. Conversely, oral insulin delivery continues to face arcane challenges, including the denaturation of insulin due to acidic gastric juice, a hostile gut mucus barrier that immobilizes and then removes particulate formulations, and a portal circulation that shunts the residue minuscule (nano)particulate dose from the bloodstream into the liver followed by macrophage activation and hepatobiliary elimination. Unless these barriers are negotiated, breaking the impasse in oral insulin delivery remains elusive. This perspective argues in favor of focusing solely on injectable insulin while deprioritizing, if not fully ceasing, further research toward developing oral insulin formulations to prevent wasting both taxpayers’ money and unethical consumption of animal lives.