Manipulating Ferroelectric Polarization and Spin Polarization of 2D CuInP2S6 Crystals for Photocatalytic CO2 Reduction.

Manipulating electronic polarizations such as ferroelectric or spin polarizations has recently emerged as an effective strategy for enhancing the efficiency of photocatalytic reactions. This study demonstrates the control of electronic polarizations modulated by ferroelectric and magnetic approaches within a two-dimensional (2D) layered crystal of copper indium thiophosphate (CuInP2S6) to boost the photocatalytic reduction of CO2. We investigate the substantial influence of ferroelectric polarization on the photocatalytic CO2 reduction efficiency, utilizing the ferroelectric-paraelectric phase transition and polarization alignment through electrical poling. Additionally, we explore enhancing the CO2 reduction efficiency by harnessing spin electrons through the synergistic introduction of sulfur vacancies and applying a magnetic field. Several advanced characterization techniques, including piezoresponse force microscopy, ultrafast pump-probe spectroscopy, in situ X-ray absorption spectroscopy, and in situ diffuse reflectance infrared Fourier transformed spectroscopy, are performed to unveil the underlying mechanism of the enhanced photocatalytic CO2 reduction. These findings pave the way for manipulating electronic polarizations regulated through ferroelectric or magnetic modulations in 2D layered materials to advance the efficiency of photocatalytic CO2 reduction.

Perihippocampal failure after hippocampal-avoidance brain radiotherapy in small cell lung cancer patients: Cases report and literature review.

RATIONALE: Brain metastasis is a major concern, and may occur in roughly 50% of patients during the clinical course of small cell lung cancer (SCLC). Because prophylactic cranial irradiation reduces the incidence of brain metastases and improves overall survival, prophylactic cranial irradiation is recommended for SCLC patients without distant metastases or an extensive stage and have responded well to systemic therapy. Hippocampal-avoidance whole-brain radiotherapy (HA-WBRT) is preferred to preserve hippocampal function while minimizing negative cognitive effects. PATIENT CONCERNS: Reducing the dose delivered to the hippocampus below the therapeutic brain dose may increase the risk of hippocampal progression; thus, HA-WBRT may be associated with a risk of perihippocampal recurrence. DIAGNOSIS: Three patients with SCLC received HA-WBRT and developed intracranial failure during clinical follow-up; 3 relapsed with intracranial failure in the perihippocampal region after 12, 13, and 7 months, respectively. INTERVENTION AND OUTCOMES: Compared to the therapeutic brain dose of cases and the underdose region around the HA region, we matched MRI scans of intracranial failure and previous planning scans of simulation and found a deviation of the underdosed region within the perihippocampal failure of approximately 55% to 63%. LESSONS: Perihippocampal failure is a rare clinical outcome in SCLC patients following HA-WBRT. Perihippocampal failure could be caused by an underdose of radiation or by the aggressiveness of the cancer itself. More research into this topic is encouraged.

Personalized prediction of intradialytic hypotension in clinical practice: Development and evaluation of a novel AI dashboard incorporating risk factors from previous and current dialysis sessions.

BACKGROUND: Intradialytic hypotension (IDH) is one of the most common and critical complications of hemodialysis. Despite many proven factors associated with IDH, accurately predicting it before it occurs for individual patients during dialysis sessions remains a challenge. PURPOSE: To establish artificial intelligence (AI) predictive models for IDH, which consider risk factors from previous and ongoing dialysis to optimize model performance. We then implement a novel digital dashboard with the best model for continuous monitoring of patients' status undergoing hemodialysis. The AI dashboard can display the real-time probability of IDH for each patient in the hemodialysis center providing an objective reference for care members for monitoring IDH and treating it in advance. METHODS: Eight machine learning (ML) algorithms, including Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), K Nearest Neighbor (KNN), Light Gradient Boosting Machine (LightGBM), Multilayer Perception (MLP), eXtreme Gradient Boosting (XGBoost), and NaiveBayes, were used to establish the predictive model of IDH to determine if the patient will acquire IDH within 60 min. In addition to real-time features, we incorporated several features sourced from previous dialysis sessions to improve the model's performance. The electronic medical records of patients who had undergone hemodialysis at Chi Mei Medical Center between September 1, 2020 and December 31, 2020 were included in this research. Impact evaluation of AI assistance was conducted by IDH rate. RESULTS: The results showed that the XGBoost model had the best performance (accuracy: 0.858, sensitivity: 0.858, specificity: 0.858, area under the curve: 0.936) and was chosen for AI dashboard implementation. The care members were delighted with the dashboard providing real-time scientific probabilities for IDH risk and historic predictive records in a graphic style. Other valuable functions were appended in the dashboard as well. Impact evaluation indicated a significant decrease in IDH rate after the application of AI assistance. CONCLUSION: This AI dashboard provides high-quality results in IDH risk prediction during hemodialysis. High-risk patients for IDH will be recognized 60 min earlier, promoting individualized preventive interventions as part of the treatment plan. Our approachis believed to promise an excellent way for IDH management.

PGC-1α drives small cell neuroendocrine cancer progression towards an ASCL1-expressing subtype with increased mitochondrial capacity.

Adenocarcinomas from multiple tissues can converge to treatment-resistant small cell neuroendocrine (SCN) cancers comprised of ASCL1, POU2F3, NEUROD1, and YAP1 subtypes. We investigated how mitochondrial metabolism influences SCN cancer (SCNC) progression. Extensive bioinformatics analyses encompassing thousands of patient tumors and human cancer cell lines uncovered enhanced expression of PGC-1α, a potent regulator of mitochondrial oxidative phosphorylation (OXPHOS), across several SCNC types. PGC-1α correlated tightly with increased expression of the lineage marker ASCL1 through a positive feedback mechanism. Analyses using a human prostate tissue-based SCN transformation system showed that the ASCL1 subtype has heightened PGC-1α expression and OXPHOS activity. PGC-1α inhibition diminished OXPHOS, reduced SCNC cell proliferation, and blocked SCN prostate tumor formation. PGC-1α overexpression enhanced OXPHOS, tripled the SCN prostate tumor formation rate, and promoted commitment to the ASCL1 lineage. These findings reveal the metabolic heterogeneity among SCNC subtypes and identify PGC-1α-induced OXPHOS as a regulator of SCNC lineage plasticity.