Corrigendum to 'Predicting post-resection recurrence by integrating imaging-based surrogates of distinct vascular patterns of hepatocellular carcinoma' (JHEP Reports 5 [2023] 100806).

[This corrects the article DOI: 10.1016/j.jhepr.2023.100806.].

Deep learning in the precise assessment of primary Sjögren's Syndrome based on ultrasound images.

OBJECTIVES: This study aimed to investigate the value of a deep learning (DL) model based on greyscale ultrasound (US) images for precise assessment and accurate diagnosis of primary Sjögren's syndrome (pSS). METHODS: This was a multicentre prospective analysis. All pSS patients were diagnosed according to 2016 ACR/EULAR criteria. 72 pSS patients and 72 sex- and age-matched healthy controls recruited between January 2022 and April 2023, together with 41 patients and 41 healthy controls recruited from June 2023 to February 2024 were used for DL model development and validation, respectively. DL model was constructed based on the ResNet 50, input with preprocessed all participants' bilateral submandibular glands (SMGs), parotid glands (PGs), and lacrimal glands (LGs) greyscale US images. Diagnostic performance of the model was compared with two radiologists. The accuracy of prediction and identification performance of DL model were evaluated by calibration curve. RESULTS: 864 and 164 greyscale US images of SMGs, PGs, and LGs were collected for development and validation of the model. The AUCs of DL model in the SMG, PG, and LG were 0.92, 0.93, 0.91 in the model cohort, and were 0.90, 0.88, 0.87 in the validation cohort respectively, outperforming both radiologists. Calibration curves showed the prediction probability of DL model were consistent with the actual probability in both model cohort and validation cohort. CONCLUSION: DL model based on greyscale US images showed diagnostic potential in the precise assessment of pSS patients in the SMG, PG, and LG, outperforming conventional radiologist evaluation.

Probucol mitigates high-fat diet-induced cognitive and social impairments by regulating brain redox and insulin resistance.

Probucol has been utilized as a cholesterol-lowering drug with antioxidative properties. However, the impact and fundamental mechanisms of probucol in obesity-related cognitive decline are unclear. In this study, male C57BL/6J mice were allocated to a normal chow diet (NCD) group or a high-fat diet (HFD) group, followed by administration of probucol to half of the mice on the HFD regimen. Subsequently, the mice were subjected to a series of behavioral assessments, alongside the measurement of metabolic and redox parameters. Notably, probucol treatment effectively alleviates cognitive and social impairments induced by HFD in mice, while exhibiting no discernible influence on mood-related behaviors. Notably, the beneficial effects of probucol arise independently of rectifying obesity or restoring systemic glucose and lipid homeostasis, as evidenced by the lack of changes in body weight, serum cholesterol levels, blood glucose, hyperinsulinemia, systemic insulin resistance, and oxidative stress. Instead, probucol could regulate the levels of nitric oxide and superoxide-generating proteins, and it could specifically alleviate HFD-induced hippocampal insulin resistance. These findings shed light on the potential role of probucol in modulating obesity-related cognitive decline and urge reevaluation of the underlying mechanisms by which probucol exerts its beneficial effects.

Evaluating Large Language Models for Drafting Emergency Department Discharge Summaries.

Importance: Large language models (LLMs) possess a range of capabilities which may be applied to the clinical domain, including text summarization. As ambient artificial intelligence scribes and other LLM-based tools begin to be deployed within healthcare settings, rigorous evaluations of the accuracy of these technologies are urgently needed. Objective: To investigate the performance of GPT-4 and GPT-3.5-turbo in generating Emergency Department (ED) discharge summaries and evaluate the prevalence and type of errors across each section of the discharge summary. Design: Cross-sectional study. Setting: University of California, San Francisco ED. Participants: We identified all adult ED visits from 2012 to 2023 with an ED clinician note. We randomly selected a sample of 100 ED visits for GPT-summarization. Exposure: We investigate the potential of two state-of-the-art LLMs, GPT-4 and GPT-3.5-turbo, to summarize the full ED clinician note into a discharge summary. Main Outcomes and Measures: GPT-3.5-turbo and GPT-4-generated discharge summaries were evaluated by two independent Emergency Medicine physician reviewers across three evaluation criteria: 1) Inaccuracy of GPT-summarized information; 2) Hallucination of information; 3) Omission of relevant clinical information. On identifying each error, reviewers were additionally asked to provide a brief explanation for their reasoning, which was manually classified into subgroups of errors. Results: From 202,059 eligible ED visits, we randomly sampled 100 for GPT-generated summarization and then expert-driven evaluation. In total, 33% of summaries generated by GPT-4 and 10% of those generated by GPT-3.5-turbo were entirely error-free across all evaluated domains. Summaries generated by GPT-4 were mostly accurate, with inaccuracies found in only 10% of cases, however, 42% of the summaries exhibited hallucinations and 47% omitted clinically relevant information. Inaccuracies and hallucinations were most commonly found in the Plan sections of GPT-generated summaries, while clinical omissions were concentrated in text describing patients' Physical Examination findings or History of Presenting Complaint. Conclusions and Relevance: In this cross-sectional study of 100 ED encounters, we found that LLMs could generate accurate discharge summaries, but were liable to hallucination and omission of clinically relevant information. A comprehensive understanding of the location and type of errors found in GPT-generated clinical text is important to facilitate clinician review of such content and prevent patient harm.

An Immunocompetent Mongolian Gerbil Model for Hepatitis E Virus Genotype 1 Infection.

BACKGROUND AND AIMS: Hepatitis E virus (HEV), primarily genotype 1 (HEV-1), causes approximately 20.1 million infections, 44,000 deaths, and 3000 stillbirths annually. Current evidence indicates that HEV-1 is only transmitted in humans. Here, we evaluated whether Mongolian gerbils can serve as animal models for HEV-1 infection. METHODS: Mongolian gerbils were used for HEV-1 and hepatitis E virus genotype 3 infection experiments. HEV infection parameters, including detection of HEV RNA and HEV antigen, liver function assessment, and histopathology, were evaluated. RESULTS: We adapted a clinical isolate of HEV-1 for Mongolian gerbils by serial passaging in feces of aged male gerbils. The gerbil-adapted strain obtained at passage 3 induced a robust, acute HEV infection, characterized by stable fecal virus shedding, elevated liver enzymes, histopathologic changes in the liver, and seroconversion to anti-HEV. An infectious complementary DNA clone of the adapted virus was generated. HEV-1-infected pregnant gerbils showed a high rate of maternal mortality and vertical transmission. HEV RNA or antigens were detected in the liver, kidney, intestine, placenta, testis, and fetus liver. Liver and placental transcriptomic analyses indicated activation of host immunity. Tacrolimus prolonged HEV-1 infection, whereas ribavirin cleared infection. The protective efficacy of a licensed HEV vaccine was validated using this model. CONCLUSIONS: HEV-1 efficiently infected Mongolian gerbils. This HEV-1 infection model will be valuable for investigating hepatitis E immunopathogenesis and evaluating vaccines and antivirals against HEV.

Tuning metal atom doped interface of electrospinning nanowires to toward fast bioelectrocatalysis.

Metal doping plays a key role in overcoming inefficient extracellular electron transfer between electrode interface and electricity-producing microorganisms. However, it is unknown whether different metals play distinctive roles in the doping process. Herein, three different metal ions (Fe, Ni and Cu) are added to the spinning precursor to obtain the corresponding electrospinning metal doped carbon nanofibers. It is found that the maximum output power of iron doped carbon nanofiber anode is 641.96 mW m-2, which is better than that of nickel doped carbon nanofiber (411.26 mW m-2) and copper doped carbon nanofiber (336.01 mW m-2), as well as 7.62 times higher than that of CNF. The results proved that due to the various number and types of active sites formed, as well as the distinction in surface morphology and structure, the electronegativity of each material is different. The different bio-abiotic interface could affect the direct contact between the anode interface and the extracellular protein of electricity producing microorganisms, which leading to a significant gap in the improvement of bioelectrocatalytic performance of different metal anode materials. This work provides a synthetic idea for designing highly efficient anode materials with directional metal modification and interface regulation.

Knowledge graph construction based on granulosa cells transcriptome from polycystic ovary syndrome with normoandrogen and hyperandrogen.

PCOS is a widespread disease that primarily caused in-pregnancy in pregnant-age women. Normoandrogen (NA) and Hyperandrogen (HA) PCOS are distinct subtypes of PCOS, while bio-markers and expression patterns for NA PCOS and HA PCOS have not been disclosed. We performed microarray analysis on granusola cells from NA PCOS, HA PCOS and normal tissue from 12 individuals. Afterwards, microarray data were processed and specific genes for NA PCOS and HA PCOS were identified. Further functional analysis selected IL6R and CD274 as new NA PCOS functional markers, and meanwhile selected CASR as new HA PCOS functional marker. IL6R, CD274 and CASR were afterwards experimentally validated on mRNA and protein level. Subsequent causal relationship analysis based on Apriori Rules Algorithm and co-occurrence methods identified classification markers for NA PCOS and HA PCOS. According to classification markers, downloaded transcriptome datasets were merged with our microarray data. Based on merged data, causal knowledge graph was constructed for NA PCOS or HA PCOS and female infertility on NA PCOS and HA PCOS. Gene-drug interaction analysis was then performed and drugs for HA PCOS and NA PCOS were predicted. Our work was among the first to indicate the NA PCOS and HA PCOS functional and classification markers and using markers to construct knowledge graphs and afterwards predict drugs for NA PCOS and HA PCOS based on transcriptome data. Thus, our study possessed biological and clinical value on further understanding the inner mechanism on the difference between NA PCOS and HA PCOS.

MRI-Based Radiomics and Deep Learning in Biological Characteristics and Prognosis of Hepatocellular Carcinoma: Opportunities and Challenges.

Hepatocellular carcinoma (HCC) is the fifth most common malignancy and the third leading cause of cancer-related death worldwide. HCC exhibits strong inter-tumor heterogeneity, with different biological characteristics closely associated with prognosis. In addition, patients with HCC often distribute at different stages and require diverse treatment options at each stage. Due to the variability in tumor sensitivity to different therapies, determining the optimal treatment approach can be challenging for clinicians prior to treatment. Artificial intelligence (AI) technology, including radiomics and deep learning approaches, has emerged as a unique opportunity to improve the spectrum of HCC clinical care by predicting biological characteristics and prognosis in the medical imaging field. The radiomics approach utilizes handcrafted features derived from specific mathematical formulas to construct various machine-learning models for medical applications. In terms of the deep learning approach, convolutional neural network models are developed to achieve high classification performance based on automatic feature extraction from images. Magnetic resonance imaging offers the advantage of superior tissue resolution and functional information. This comprehensive evaluation plays a vital role in the accurate assessment and effective treatment planning for HCC patients. Recent studies have applied radiomics and deep learning approaches to develop AI-enabled models to improve accuracy in predicting biological characteristics and prognosis, such as microvascular invasion and tumor recurrence. Although AI-enabled models have demonstrated promising potential in HCC with biological characteristics and prognosis prediction with high performance, one of the biggest challenges, interpretability, has hindered their implementation in clinical practice. In the future, continued research is needed to improve the interpretability of AI-enabled models, including aspects such as domain knowledge, novel algorithms, and multi-dimension data sources. Overcoming these challenges would allow AI-enabled models to significantly impact the care provided to HCC patients, ultimately leading to their deployment for clinical use. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.

Development of a radiomics-based model to predict occult liver metastases of pancreatic ductal adenocarcinoma: a multicenter study.

BACKGROUND: Undetectable occult liver metastases block the long-term survival of pancreatic ductal adenocarcinoma (PDAC). This study aimed to develop a radiomics-based model to predict occult liver metastases and assess its prognostic capacity for survival. MATERIALS AND METHODS: Patients who underwent surgical resection and were pathologically proven with PDAC were recruited retrospectively from five tertiary hospitals between January 2015 and December 2020. Radiomics features were extracted from tumors, and the radiomics-based model was developed in the training cohort using LASSO-logistic regression. The model's performance was assessed in the internal and external validation cohorts using the area under the receiver operating curve (AUC). Subsequently, the association of the model's risk stratification with progression-free survival (PFS) and overall survival (OS) was then statistically examined using Cox regression analysis and the log-rank test. RESULTS: A total of 438 patients [mean (SD) age, 62.0 (10.0) years; 255 (58.2%) male] were divided into the training cohort ( n =235), internal validation cohort ( n =100), and external validation cohort ( n =103). The radiomics-based model yielded an AUC of 0.73 (95% CI: 0.66-0.80), 0.72 (95% CI: 0.62-0.80), and 0.71 (95% CI: 0.61-0.80) in the training, internal validation, and external validation cohorts, respectively, which were higher than the preoperative clinical model. The model's risk stratification was an independent predictor of PFS (all P <0.05) and OS (all P <0.05). Furthermore, patients in the high-risk group stratified by the model consistently had a significantly shorter PFS and OS at each TNM stage (all P <0.05). CONCLUSION: The proposed radiomics-based model provided a promising tool to predict occult liver metastases and had a great significance in prognosis.

Growing up without health insurance: Evidence from rural China.

This paper examines the long-term effects of health insurance on children's educational attainment in a developing country. Utilizing the county-by-county rollout of the New Rural Cooperative Medical Scheme in rural China, we find that exposure to the health insurance program in early life leads to improved educational attainment in adulthood. Empirical tests suggest that a short-term increase in health care utilization is unlikely to be a potential channel. We provide some evidence for the channel that health insurance reduces household financial burdens and increases household expenditure on children's nutrition.

A DFT study of sulforaphane adsorption on the group III nitrides (B12N12, Al12N12 and Ga12N12) nanocages.

In this paper, the adsorption behavior of group III nitrides (B12N12, Al12N12, and Ga12N12) nanocages to sulforaphane (SF) anticancer medicine were studied by density functional theory (DFT). The adsorption energy, solvation energy, desorption time and related quantum molecular descriptors were calculated in neutral and acidic solutions. When the drugs were adsorbed to nanocages, the structure of nanocages and drugs changed after adsorption, indicating that the process was effective adsorption. The adsorption energy and solvation energy of the complexes created after adsorption were negative values, which indicated that the structure of complexes formed by adsorption were stable. According to charge decomposition analysis (CDA) and natural bonding orbitals (NBO), drugs act as charge donors and nanocages act as charge acceptors, so that the charge flows from drugs to nanocages. Thermodynamic calculations demonstrate that drugs adsorption on nanocages is a spontaneous exothermic process. The calculation of quantum molecular descriptors confirmed that drugs adsorption on nanocages increased the chemical reactivity and solubility of drugs, which facilitated its transfer in biological fluids. Both interaction region index (IRI) and topological analysis of atom in molecule (AIM) revealed Van Der Waals interaction between drugs and nanocages. Protonation studies demonstrated that acidic circumstances could improve the polarity of complexes, increase the solvation effect, and boost drugs release in target cancer cells. The results of this work indicate that X12N12(X = B, Al, Ga) nanocages can be used as the delivery vehicle of SF drug.Communicated by Ramaswamy H. Sarma.

Interfacial fracture toughness of different surface treatments on zirconia-reinforced lithium silicate glass-ceramics.

This study investigated the influence of different surface treatments on unfiring or firing zirconia-reinforced lithium silicate (ZLS) glass-ceramics. Celtra Duo and IPS e.max CAD blocks were cut and process following manufacturer protocols. The specimen surface was treated with seven different protocols. Two ceramic blocks with the same surface treatment were bonded with luting agent and prepared for mini-interfacial fracture toughness tests (mini-iFT). The specimens were tested after 1-week storage. The data was statistically analyzed using two-way ANOVA and Dunnett's T3 comparison (α=0.05). The highest mini-iFT of both Celtra Duo unfired and fired was shown in the HF+S group, which was not significantly different from HF+S+UA. For IPS e.max CAD, the mini-iFT was higher in the groups treated with hydrofluoric acid. Additional adhesive after silane application did not significantly improve bonding effectiveness. Therefore, surface treatment with hydrofluoric acid and silane is recommended for both unfiring and firing ZLS glass ceramics.

mRNA vaccines in disease prevention and treatment.

mRNA vaccines have emerged as highly effective strategies in the prophylaxis and treatment of diseases, thanks largely although not totally to their extraordinary performance in recent years against the worldwide plague COVID-19. The huge superiority of mRNA vaccines regarding their efficacy, safety, and large-scale manufacture encourages pharmaceutical industries and biotechnology companies to expand their application to a diverse array of diseases, despite the nonnegligible problems in design, fabrication, and mode of administration. This review delves into the technical underpinnings of mRNA vaccines, covering mRNA design, synthesis, delivery, and adjuvant technologies. Moreover, this review presents a systematic retrospective analysis in a logical and well-organized manner, shedding light on representative mRNA vaccines employed in various diseases. The scope extends across infectious diseases, cancers, immunological diseases, tissue damages, and rare diseases, showcasing the versatility and potential of mRNA vaccines in diverse therapeutic areas. Furthermore, this review engages in a prospective discussion regarding the current challenge and potential direction for the advancement and utilization of mRNA vaccines. Overall, this comprehensive review serves as a valuable resource for researchers, clinicians, and industry professionals, providing a comprehensive understanding of the technical aspects, historical context, and future prospects of mRNA vaccines in the fight against various diseases.

Predicting post-resection recurrence by integrating imaging-based surrogates of distinct vascular patterns of hepatocellular carcinoma.

Background & Aims: Distinct vascular patterns, including microvascular invasion (MVI) and vessels encapsulating tumour clusters (VETC), are associated with poor outcomes of hepatocellular carcinoma (HCC). Imaging surrogates of these vascular patterns potentially help to predict post-resection recurrence. Herein, a prognostic model integrating imaging-based surrogates of these distinct vascular patterns was developed to predict postoperative recurrence-free survival (RFS) in patients with HCC. Methods: Clinico-radiological data of 1,285 patients with HCC from China undergoing surgical resection were retrospectively enrolled from seven medical centres between 2014 and 2020. A prognostic model using clinical data and imaging-based surrogates of MVI and VETC patterns was developed (n = 297) and externally validated (n = 373) to predict RFS. The surrogates (i.e. MVI and VETC scores) were individually built from preoperative computed tomography using two independent cohorts (n = 360 and 255). Whether the model's stratification was associated with postoperative recurrence following anatomic resection was also evaluated. Results: The MVI and VETC scores demonstrated effective performance in their respective training and validation cohorts (AUC: 0.851-0.883 for MVI and 0.834-0.844 for VETC). The prognostic model incorporating serum alpha-foetoprotein, tumour multiplicity, MVI score, and VETC score achieved a C-index of 0.748-0.764 for the developing and external validation cohorts and generated three prognostically distinct strata. For patients at model-predicted medium risk, anatomic resection was associated with improved RFS (p <0.05). By contrast, anatomic resection had no impact on RFS in patients at model-predicted low or high risk (both p >0.05). Conclusions: The proposed model integrating imaging-based surrogates of distinct vascular patterns enabled accurate prediction for RFS. It can potentially be used to identify HCC surgical candidates who may benefit from anatomic resection. Impact and implications: MVI and VETC are distinct vascular patterns of HCC associated with aggressive biological behaviour and poor outcomes. Our multicentre study provided a model incorporating imaging-based surrogates of these patterns for preoperatively predicting RFS. The proposed model, which uses imaging detection to estimate the risk of MVI and VETC, offers an opportunity to help shed light on the association between tumour aggressiveness and prognosis and to support the selection of the appropriate type of surgical resection.

An imaging-based machine learning model outperforms clinical risk scores for prognosis of cirrhotic variceal bleeding.

OBJECTIVES: To develop and validate a machine learning model based on contrast-enhanced CT to predict the risk of occurrence of the composite clinical endpoint (hospital-based intervention or death) in cirrhotic patients with acute variceal bleeding (AVB). METHODS: This retrospective study enrolled 330 cirrhotic patients with AVB between January 2017 and December 2020 from three clinical centers. Contrast-enhanced CT and clinical data were collected. Centers A and B were divided 7:3 into a training set and an internal test set, and center C served as a separate external test set. A well-trained deep learning model was applied to segment the liver and spleen. Then, we extracted 106 original features of the liver and spleen separately based on the Image Biomarker Standardization Initiative (IBSI). We constructed the Liver-Spleen (LS) model based on the selected radiomics features. The performance of LS model was evaluated by receiver operating characteristics and calibration curves. The clinical utility of models was analyzed using decision curve analyses (DCA). RESULTS: The LS model demonstrated the best diagnostic performance in predicting the composite clinical endpoint of AVB in patients with cirrhosis, with an AUC of 0.782 (95% CI 0.650-0.882) and 0.789 (95% CI 0.674-0.878) in the internal test and external test groups, respectively. Calibration curves and DCA indicated the LS model had better performance than traditional clinical scores. CONCLUSION: A novel machine learning model outperforms previously known clinical risk scores in assessing the prognosis of cirrhotic patients with AVB CLINICAL RELEVANCE STATEMENT: The Liver-Spleen model based on contrast-enhanced CT has proven to be a promising tool to predict the prognosis of cirrhotic patients with acute variceal bleeding, which can facilitate decision-making and personalized therapy in clinical practice. KEY POINTS: • The Liver-Spleen machine learning model (LS model) showed good performance in assessing the clinical composite endpoint of cirrhotic patients with AVB (AUC ≥ 0.782, sensitivity ≥ 80%). • The LS model outperformed the clinical scores (AUC ≤ 0.730, sensitivity ≤ 70%) in both internal and external test cohorts.

Transcriptional control of pancreatic cancer immunosuppression by metabolic enzyme CD73 in a tumor-autonomous and -autocrine manner.

Cancer cell metabolism contributes to the establishment of an immunosuppressive tumor microenvironment. Aberrant expression of CD73, a critical enzyme in ATP metabolism, on the cell surface results in the extracellular accumulation of adenosine, which exhibits direct inhibitory effects on tumor-infiltrating lymphocytes. However, little is known about the influence of CD73 on negative immune regulation-associated signaling molecules and transduction pathways inside tumor cells. This study aims to demonstrate the moonlighting functions of CD73 in immunosuppression in pancreatic cancer, an ideal model characterized by complex crosstalk among cancer metabolism, immune microenvironment, and immunotherapeutic resistance. The synergistic effect of CD73-specific drugs in combination with immune checkpoint blockade is observed in multiple pancreatic cancer models. Cytometry by time-of-flight analysis shows that CD73 inhibition reduces tumor-infiltrating Tregs in pancreatic cancer. Tumor cell-autonomous CD73 is found to facilitate Treg recruitment, in which CCL5 is identified as a significant downstream effector of CD73 using integrated proteomic and transcriptomic analyses. CD73 transcriptionally upregulates CCL5 through tumor cell-autocrine adenosine-Adora2a signaling-mediated activation of the p38-STAT1 axis, recruiting Tregs to pancreatic tumors and causing an immunosuppressive microenvironment. Together, this study highlights that CD73-adenosine metabolism transcriptionally controls pancreatic cancer immunosuppression in a tumor-autonomous and -autocrine manner.

Predicting Microvascular Invasion in Hepatocellular Carcinoma Using CT-based Radiomics Model.

Background Prediction of microvascular invasion (MVI) may help determine treatment strategies for hepatocellular carcinoma (HCC). Purpose To develop a radiomics approach for predicting MVI status based on preoperative multiphase CT images and to identify MVI-associated differentially expressed genes. Materials and Methods Patients with pathologically proven HCC from May 2012 to September 2020 were retrospectively included from four medical centers. Radiomics features were extracted from tumors and peritumor regions on preoperative registration or subtraction CT images. In the training set, these features were used to build five radiomics models via logistic regression after feature reduction. The models were tested using internal and external test sets against a pathologic reference standard to calculate area under the receiver operating characteristic curve (AUC). The optimal AUC radiomics model and clinical-radiologic characteristics were combined to build the hybrid model. The log-rank test was used in the outcome cohort (Kunming center) to analyze early recurrence-free survival and overall survival based on high versus low model-derived score. RNA sequencing data from The Cancer Image Archive were used for gene expression analysis. Results A total of 773 patients (median age, 59 years; IQR, 49-64 years; 633 men) were divided into the training set (n = 334), internal test set (n = 142), external test set (n = 141), outcome cohort (n = 121), and RNA sequencing analysis set (n = 35). The AUCs from the radiomics and hybrid models, respectively, were 0.76 and 0.86 for the internal test set and 0.72 and 0.84 for the external test set. Early recurrence-free survival (P < .01) and overall survival (P < .007) can be categorized using the hybrid model. Differentially expressed genes in patients with findings positive for MVI were involved in glucose metabolism. Conclusion The hybrid model showed the best performance in prediction of MVI. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Summers in this issue.

The potential of ferroptosis combined with radiotherapy in cancer treatment.

Ferroptosis is a new form of regulatory cell death that is closely related to the balance of redox reactions and the occurrence and development of cancer. There is increasing evidence that inducing ferroptosis in cells has great potential in the treatment of cancer. Especially when combined with traditional therapy, it can improve the sensitivity of cancer cells to traditional therapy and overcome the drug resistance of cancer cells. This paper reviews the signaling pathways regulating ferroptosis and the great potential of ferroptosis and radiotherapy (RT) in cancer treatment and emphasizes the unique therapeutic effects of ferroptosis combined with RT on cancer cells, such as synergy, sensitization and reversal of drug resistance, providing a new direction for cancer treatment. Finally, the challenges and research directions for this joint strategy are discussed.

The social scar of the pandemic: Impacts of COVID-19 exposure on interpersonal trust.

This paper employs a difference-in-differences strategy to examine the causal effect of exposure to the COVID-19 pandemic on interpersonal trust amidst zero-COVID policies in China. Using a nationally representative panel survey, we find that COVID-19 exposure leads to a decrease in the levels of generalized trust. We also show that the change in interpersonal trust varies across domains. Specifically, COVID-19 exposure significantly decreases trust in parents, neighbors, and local government officials, but has small and insignificant effects on trust in doctors, strangers, and Americans. Empirical tests suggest that changes in income and physical health status are not likely to be potential channels. We provide some evidence for the mechanism of deteriorated mental health status and pessimistic expectations.

Personalized pancreatic cancer therapy: from the perspective of mRNA vaccine.

Pancreatic cancer is characterized by inter-tumoral and intra-tumoral heterogeneity, especially in genetic alteration and microenvironment. Conventional therapeutic strategies for pancreatic cancer usually suffer resistance, highlighting the necessity for personalized precise treatment. Cancer vaccines have become promising alternatives for pancreatic cancer treatment because of their multifaceted advantages including multiple targeting, minimal nonspecific effects, broad therapeutic window, low toxicity, and induction of persistent immunological memory. Multiple conventional vaccines based on the cells, microorganisms, exosomes, proteins, peptides, or DNA against pancreatic cancer have been developed; however, their overall efficacy remains unsatisfactory. Compared with these vaccine modalities, messager RNA (mRNA)-based vaccines offer technical and conceptional advances in personalized precise treatment, and thus represent a potentially cutting-edge option in novel therapeutic approaches for pancreatic cancer. This review summarizes the current progress on pancreatic cancer vaccines, highlights the superiority of mRNA vaccines over other conventional vaccines, and proposes the viable tactic for designing and applying personalized mRNA vaccines for the precise treatment of pancreatic cancer.