Development of 4-phenylbutyric acid microsponge gel formulations for the treatment of lewisite-mediated skin injury.

Lewisite, a chemical warfare agent, causes skin blisters, erythema, edema, and inflammation, requiring mitigation strategies in case of accidental or deliberate exposure. 4-phenyl butyric acid (4-PBA), a chemical chaperone, reduces endoplasmic reticulum stress and skin inflammation. The study aimed to encapsulate 4-PBA in microsponges for effective, sustained delivery against lewisite injury. Porous microsponges in a topical gel would potentially sustain delivery and improve residence time on the skin. Microsponges were developed using the quasi-emulsion solvent diffusion method with Eudragit RS100. Optimized formulation showed 10.58%w/w drug loading was incorporated in a carboxymethylcellulose (CMC) and Carbopol gel for in vitro release and permeation testing using dermatomed human skin. A sustained release was obtained from all vehicles in the release study, and IVPT results showed that compared to the control (41.52 ± 2.54 µg/sq.cm), a sustained permeation profile with a reduced delivery was observed for microsponges in PBS (14.16 ± 1.23 µg/sq.cm) along with Carbopol 980 gel (12.55 ± 1.41 µg/sq.cm), and CMC gel (10.09 ± 1.23 µg/sq.cm) at 24 h. Optimized formulation showed significant protection against lewisite surrogate phenyl arsine oxide (PAO) challenged skin injury in Ptch1+/-/SKH-1 hairless mice at gross and molecular levels. A reduction in Draize score by 29%, a reduction in skin bifold thickness by 8%, a significant reduction in levels of IL-1ß, IL6, and GM-CSF by 54%, 30%, and 55%, respectively, and a reduction in apoptosis by 31% was observed. Thus, the translational feasibility of 4-PBA microsponges for effective, sustained delivery against lewisite skin injury is demonstrated.

Exploration of sialic acid receptors as a potential target for cancer treatment: A comprehensive review.

The potential to target anticancer drugs directly to cancer cells is the most difficult challenge in the current scenario. Progressive works are being done on multifarious receptors and are on the horizon, expected to facilitate tailored treatment for cancer. Among several receptors, one is the sialic acid (SA) receptor by which cancer cells can be targeted directly as hyper sialylation is one of the most distinguishing characteristics of cancer cells. SA receptors have shown tremendous potential for tumor targeting because of their elevated expression in a range of human malignancies including prostate, breast, gastric cells, myeloid leukemia, liver, etc. This article reviews the overexpression of SA receptors in various tumors and diverse strategies for targeting these receptors to deliver drugs, enzymes, and genes for therapeutic applications. It also summarizes the diagnostic applications of SA-grafted nanoparticles for imaging various SA-overexpressing cancer cells and technological advances that are propelling sialic acid to the forefront of cancer therapy.

RadBERT-CL: Factually-Aware Contrastive Learning For Radiology Report Classification.

Radiology reports are unstructured and contain the imaging findings and corresponding diagnoses transcribed by radiologists which include clinical facts and negated and/or uncertain statements. Extracting pathologic findings and diagnoses from radiology reports is important for quality control, population health, and monitoring of disease progress. Existing works, primarily rely either on rule-based systems or transformer-based pre-trained model fine-tuning, but could not take the factual and uncertain information into consideration, and therefore generate false positive outputs. In this work, we introduce three sedulous augmentation techniques which retain factual and critical information while generating augmentations for contrastive learning. We introduce RadBERT-CL, which fuses these information into BlueBert via a self-supervised contrastive loss. Our experiments on MIMIC-CXR show superior performance of RadBERT-CL on fine-tuning for multi-class, multi-label report classification. We illustrate that when few labeled data are available, RadBERT-CL outperforms conventional SOTA transformers (BERT/BlueBert) by significantly larger margins (6-11%). We also show that the representations learned by RadBERT-CL can capture critical medical information in the latent space.