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.

Objectives: Sepsis is a leading global cause of mortality, and predicting its outcomes is vital for improving patient care. This study explored the capabilities of ChatGPT, a state-of-the-art natural language processing model, in predicting in-hospital mortality for sepsis patients. Methods: This study utilized data from the Korean Sepsis Alliance (KSA) database, collected between 2019 and 2021, focusing on adult intensive care unit (ICU) patients and aiming to determine whether ChatGPT could predict all-cause mortality after ICU admission at 7 and 30 days. Structured prompts enabled ChatGPT to engage in in-context learning, with the number of patient examples varying from zero to six. The predictive capabilities of ChatGPT-3.5-turbo and ChatGPT-4 were then compared against a gradient boosting model (GBM) using various performance metrics. Results: From the KSA database, 4,786 patients formed the 7-day mortality prediction dataset, of whom 718 died, and 4,025 patients formed the 30-day dataset, with 1,368 deaths. Age and clinical markers (e.g., Sequential Organ Failure Assessment score and lactic acid levels) showed significant differences between survivors and non-survivors in both datasets. For 7-day mortality predictions, the area under the receiver operating characteristic curve (AUROC) was 0.70–0.83 for GPT-4, 0.51–0.70 for GPT-3.5, and 0.79 for GBM. The AUROC for 30-day mortality was 0.51–0.59 for GPT-4, 0.47–0.57 for GPT-3.5, and 0.76 for GBM. Zero-shot predictions using GPT-4 for mortality from ICU admission to day 30 showed AUROCs from the mid-0.60s to 0.75 for GPT-4 and mainly from 0.47 to 0.63 for GPT-3.5. Conclusions GPT-4 demonstrated potential in predicting short-term in-hospital mortality, although its performance varied across different evaluation metrics.

Evaluating cell metabolism is crucial during pluripotent stem cell (PSC) differentiation and somatic cell reprogramming as it affects cell fate. As cultured stem cells are heterogeneous, a comparative analysis of relative metabolism using existing metabolic analysis methods is difficult, resulting in inaccuracies. In this study, we measured human PSC basal metabolic levels using a Seahorse analyzer. We used fibroblasts, human induced PSCs, and human embryonic stem cells to monitor changes in basal metabolic levels according to cell number and determine the number of cells suitable for analysis. We evaluated normalization methods using glucose and selected the most suitable for the metabolic analysis of heterogeneous PSCs during the reprogramming stage. The response of fibroblasts to glucose increased with starvation time, with oxygen consumption rate and extracellular acidification rate responding most effectively to glucose 4 hours after starvation and declining after 5 hours of starvation. Fibroblasts and PSCs achieved appropriate responses to glucose without damaging their metabolism 2∼4 and 2∼3 hours after starvation, respectively. We developed a novel method for comparing basal metabolic rates of fibroblasts and PSCs, focusing on quantitative analysis of glycolysis and oxidative phosphorylation using glucose without enzyme inhibitors. This protocol enables efficient comparison of energy metabolism among cell types, including undifferentiated PSCs, differentiated cells, and cells undergoing cellular reprogramming, and addresses critical issues, such as differences in basal metabolic levels and sensitivity to normalization, providing valuable insights into cellular energetics.

Background: Worldwide, sepsis is the leading cause of death in hospitals. If mortality rates in patients with sepsis can be predicted early, medical resources can be allocated efficiently. We constructed machine learning (ML) models to predict the mortality of patients with sepsis in a hospital emergency department. Methods: This study prospectively collected nationwide data from an ongoing multicenter cohort of patients with sepsis identified in the emergency department. Patients were enrolled from 19 hospitals between September 2019 and December 2020. For acquired data from 3,657 survivors and 1,455 deaths, six ML models (logistic regression, support vector machine, random forest, extreme gradient boosting [XGBoost], light gradient boosting machine, and categorical boosting [CatBoost]) were constructed using fivefold cross-validation to predict mortality. Through these models, 44 clinical variables measured on the day of admission were compared with six sequential organ failure assessment (SOFA) components (PaO 2 /FIO 2 [PF], platelets (PLT), bilirubin, cardiovascular, Glasgow Coma Scale score, and creatinine).The confidence interval (CI) was obtained by performing 10,000 repeated measurements via random sampling of the test dataset. All results were explained and interpreted using Shapley’s additive explanations (SHAP). Results: Of the 5,112 participants, CatBoost exhibited the highest area under the curve (AUC) of 0.800 (95% CI, 0.756–0.840) using clinical variables. Using the SOFA components for the same patient, XGBoost exhibited the highest AUC of 0.678 (95% CI, 0.626–0.730). As interpreted by SHAP, albumin, lactate, blood urea nitrogen, and international normalization ratio were determined to significantly affect the results. Additionally, PF and PLTs in the SOFA component significantly influenced the prediction results. Conclusion Newly established ML-based models achieved good prediction of mortality in patients with sepsis. Using several clinical variables acquired at the baseline can provide more accurate results for early predictions than using SOFA components. Additionally, the impact of each variable was identified.

Cervical disc arthroplasty (CDA) has become more widespread and diverges from the conventional technique used in anterior cervical fusion for cervical degenerative disc disease. As arthroplasty has become a popular treatment option, few complications have been reported in the literature. These include subsidence, expulsion, posterior avulsion fractures, heterotopic ossification, and osteolysis. One of the critical complications is osteolysis, but current studies on this subject are limited in terms of not elucidating the incidence, etiology, and consequences. The authors present two cases, who presented with clinical signs of gradually worsening myelopathy induced by posterior vertebral body osteolysis, 2 years after CDA. Subsequently, the patient underwent posterior decompression and fusion without prosthesis removal. Postoperatively, the clinical symptoms gradually resolved, with no severe deficits. The present rare cases highlight the osteolysis that occurs after CDA, which can cause cervical myelopathy, and suggest spine surgeons to be alert to this fatal complication.

Background/Aims: Gastroesophageal reflux disease (GERD) is a common chronic gastrointestinal disorder that typically requires long-term maintenance therapy. However, little is known about patient preferences and satisfaction and real-world prescription patterns regarding maintenance therapy for GERD. Methods: This observational, cross-sectional, multicenter study involved patients from 18 referral hospitals in Korea. We surveyed patients who had been prescribed proton pump inhibitors (PPIs) for GERD for at least 90 days with a minimum follow-up duration of 1 year. The main outcome was overall patient satisfaction with different maintenance therapy modalities. Results: A total of 197 patients were enrolled. Overall patient satisfaction, patient preferences, and GERD health-related quality of life scores did not significantly differ among the maintenance therapy modality groups. However, the on-demand therapy group experienced a significantly longer disease duration than the continuous therapy group. The continuous therapy group demonstrated a lower level of awareness of potential adverse effects associated with PPIs than the on-demand therapy group but received higher doses of PPIs than the on-demand therapy group. The prescribed doses of PPIs also varied based on the phenotype of GERD, with higher doses prescribed for non-erosive reflux disease than erosive reflux disease. Conclusion Although overall patient satisfaction did not significantly differ among the different PPI maintenance therapy modality groups, awareness of potential adverse effects was significantly different between the on-demand and continuous therapy groups.