In light of the burgeoning successes of cancer immunotherapy, glioblastoma (GBM) remains refractory due to an immunosuppressive microenvironment originating from its molecular heterogeneity. Thus, identifying promising therapeutic targets for treating GBM and discovering methodologies to effectively regulate them is still a tremendous challenge. Here we describe photodynamic protein tyrosine phosphatase 1B (PTP1B) proteolysis mediated by a proteolysis-targeting chimera (PROTAC) nanoassembly. The PTP1B-targeting PROTAC is conjugated with a photosensitizer via a cathepsin B (Cat B)-cleavable peptide, which spontaneously forms nanoassemblies due to intermolecular π-π stacking interactions. In GBM models, PROTAC nanoassemblies significantly accumulate in the tumor region across the disrupted blood-brain barrier (BBB), triggering a burst release of the photosensitizer and active PROTAC by Cat B-mediated enzymatic cleavage. Upon laser irradiation, photodynamic therapy (PDT) synergizes with PROTAC-mediated PTP1B proteolysis to induce potent immunogenic cell death (ICD) in tumor cells. Subsequently, persistent PTP1B degradation by nanoassemblies in Cat B-overexpressed intratumoral T cells downregulates exhaustion markers, reinvigorating their functionality. These sequential processes of photodynamic PTP1B proteolysis ultimately augment T cell-mediated antitumor immunity as well as protective immunity, completely eradicating the primary GBM and preventing its recurrence. Overall, our findings underscore the therapeutic potential of combining PDT with PROTAC activity for GBM immunotherapy.

The depth of double-lumen endobronchial tube (DLT) is reportedly known tobe directly proportional to height and several height-based recommendations have beensuggested. This retrospective study was designed to find out the difference between calculated depths using height-based formulae and realistic depths in clinical practice of DLTplacement by analyzing pooled data from patients intubated with left-sided DLT.Methods: The electronic medical records of adults, intubated with DLT from February 2018to December 2020, were reviewed. Data retrieved included age, sex, height, weight, andsize and depth of DLT. The finally documented DLT depth (depth final, DF) was comparedwith the calculated depths, and the relationship between height and DF was also evaluated.A questionnaire on endobronchial intubation method was sent to anesthesiologists.Results: A total of 503 out of 575 electronic records of consecutive patients were analyzed.Although the relationship between height and DF was shown to have significant correlation(Spearman’s rho = 0.63, P < 0.001), DF was shown to be significantly greater than calculated depths (P < 0.001). Despite 57.1% of anesthesiologists have knowledge of clinical recommendations to anticipate size and depth of DLT, no one routinely utilizes those recommendations.Conclusions: Anesthesiologists tend to place DLTs in a deeper position than expected whendepths are calculated using height-based recommendations. Although such discrepanciesmay not be clinically meaningful, efforts are needed to standardize the methods of endobronchial intubation to prevent potential complications associated with malposition.

Purpose: Assessing the status of metastasis in sentinel lymph nodes (SLNs) by pathologists is an essential task for the accurate staging of breast cancer. However, histopathological evaluation of sentinel lymph nodes by a pathologist is not easy and is a tedious and time-consuming task. The purpose of this study is to review a challenge competition (HeLP 2018) to develop automated solutions for the classification of metastases in hematoxylin and eosin–stained frozen tissue sections of SLNs in breast cancer patients. Materials and Methods: A total of 297 digital slides were obtained from frozen SLN sections, which include post–neoadjuvant cases (n = 144, 48.5%) in Asan Medical Center, South Korea. The slides were divided into training, development, and validation sets. All of the imaging datasets have been manually segmented by expert pathologists. A total of 10 participants were allowed to use the Kakao challenge platform for six weeks with two P40 GPUs. The algorithms were assessed in terms of the AUC (area under receiver operating characteristic curve). Results: The top three teams showed 0.986, 0.985, and 0.945 AUCs for the development set and 0.805, 0.776, and 0.765 AUCs for the validation set. Micrometastatic tumors, neoadjuvant systemic therapy, invasive lobular carcinoma, and histologic grade 3 were associated with lower diagnostic accuracy. Conclusion In a challenge competition, accurate deep learning algorithms have been developed, which can be helpful in making frozen diagnosis of intraoperative sentinel lymph node biopsy. Whether this approach has clinical utility will require evaluation in a clinical setting