West China Medical Center of Sichuan University

autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

Ferroptosis is an iron-dependent cell death, which is different from apoptosis, necrosis, autophagy, and other forms of cell death. The process of ferroptotic cell death is defined by the accumulation of lethal lipid species derived from the peroxidation of lipids, which can be prevented by iron chelators (e.g., deferiprone, deferoxamine) and small lipophilic antioxidants (e.g., ferrostatin, liproxstatin). This review summarizes current knowledge about the regulatory mechanism of ferroptosis and its association with several pathways, including iron, lipid, and cysteine metabolism. We have further discussed the contribution of ferroptosis to the pathogenesis of several diseases such as cancer, ischemia/reperfusion, and various neurodegenerative diseases (e.g., Alzheimer's disease and Parkinson's disease), and evaluated the therapeutic applications of ferroptosis inhibitors in clinics.

Throughout its 40-year history, the field of gene therapy has been marked by many transitions. It has seen great strides in combating human disease, has given hope to patients and families with limited treatment options, but has also been subject to many setbacks. Treatment of patients with this class of investigational drugs has resulted in severe adverse effects and, even in rare cases, death. At the heart of this dichotomous field are the viral-based vectors, the delivery vehicles that have allowed researchers and clinicians to develop powerful drug platforms, and have radically changed the face of medicine. Within the past 5 years, the gene therapy field has seen a wave of drugs based on viral vectors that have gained regulatory approval that come in a variety of designs and purposes. These modalities range from vector-based cancer therapies, to treating monogenic diseases with life-altering outcomes. At present, the three key vector strategies are based on adenoviruses, adeno-associated viruses, and lentiviruses. They have led the way in preclinical and clinical successes in the past two decades. However, despite these successes, many challenges still limit these approaches from attaining their full potential. To review the viral vector-based gene therapy landscape, we focus on these three highly regarded vector platforms and describe mechanisms of action and their roles in treating human disease.

Abstract Nicotinamide adenine dinucleotide (NAD + ) and its metabolites function as critical regulators to maintain physiologic processes, enabling the plastic cells to adapt to environmental changes including nutrient perturbation, genotoxic factors, circadian disorder, infection, inflammation and xenobiotics. These effects are mainly achieved by the driving effect of NAD + on metabolic pathways as enzyme cofactors transferring hydrogen in oxidation-reduction reactions. Besides, multiple NAD + -dependent enzymes are involved in physiology either by post-synthesis chemical modification of DNA, RNA and proteins, or releasing second messenger cyclic ADP-ribose (cADPR) and NAADP + . Prolonged disequilibrium of NAD + metabolism disturbs the physiological functions, resulting in diseases including metabolic diseases, cancer, aging and neurodegeneration disorder. In this review, we summarize recent advances in our understanding of the molecular mechanisms of NAD + -regulated physiological responses to stresses, the contribution of NAD + deficiency to various diseases via manipulating cellular communication networks and the potential new avenues for therapeutic intervention.

Recent advances in neoantigen research have accelerated the development and regulatory approval of tumor immunotherapies, including cancer vaccines, adoptive cell therapy and antibody-based therapies, especially for solid tumors. Neoantigens are newly formed antigens generated by tumor cells as a result of various tumor-specific alterations, such as genomic mutation, dysregulated RNA splicing, disordered post-translational modification, and integrated viral open reading frames. Neoantigens are recognized as non-self and trigger an immune response that is not subject to central and peripheral tolerance. The quick identification and prediction of tumor-specific neoantigens have been made possible by the advanced development of next-generation sequencing and bioinformatic technologies. Compared to tumor-associated antigens, the highly immunogenic and tumor-specific neoantigens provide emerging targets for personalized cancer immunotherapies, and serve as prospective predictors for tumor survival prognosis and immune checkpoint blockade responses. The development of cancer therapies will be aided by understanding the mechanism underlying neoantigen-induced anti-tumor immune response and by streamlining the process of neoantigen-based immunotherapies. This review provides an overview on the identification and characterization of neoantigens and outlines the clinical applications of prospective immunotherapeutic strategies based on neoantigens. We also explore their current status, inherent challenges, and clinical translation potential.

Oral squamous cell carcinoma (OSCC) develops on the mucosal epithelium of the oral cavity. It accounts for approximately 90% of oral malignancies and impairs appearance, pronunciation, swallowing, and flavor perception. In 2020, 377,713 OSCC cases were reported globally. According to the Global Cancer Observatory (GCO), the incidence of OSCC will rise by approximately 40% by 2040, accompanied by a growth in mortality. Persistent exposure to various risk factors, including tobacco, alcohol, betel quid (BQ), and human papillomavirus (HPV), will lead to the development of oral potentially malignant disorders (OPMDs), which are oral mucosal lesions with an increased risk of developing into OSCC. Complex and multifactorial, the oncogenesis process involves genetic alteration, epigenetic modification, and a dysregulated tumor microenvironment. Although various therapeutic interventions, such as chemotherapy, radiation, immunotherapy, and nanomedicine, have been proposed to prevent or treat OSCC and OPMDs, understanding the mechanism of malignancies will facilitate the identification of therapeutic and prognostic factors, thereby improving the efficacy of treatment for OSCC patients. This review summarizes the mechanisms involved in OSCC. Moreover, the current therapeutic interventions and prognostic methods for OSCC and OPMDs are discussed to facilitate comprehension and provide several prospective outlooks for the fields.

With the rapid development of “Internet plus”, medical care has entered the era of big data. However, there is little research on medical big data (MBD) from the perspectives of bibliometrics and visualization. The substantive research on the basic aspects of MBD itself is also rare. This study aims to explore the current status of medical big data through visualization analysis on the journal papers related to MBD. We analyze a total of 988 references which were downloaded from the Science Citation Index Expanded and the Social Science Citation Index databases from Web of Science and the time span was defined as “all years”. The GraphPad Prism 5, VOSviewer and CiteSpace softwares are used for analysis. Many results concerning the annual trends, the top players in terms of journal and institute levels, the citations and H-index in terms of country level, the keywords distribution, the highly cited papers, the co-authorship status and the most influential journals and authors are presented in this paper. This study points out the development status and trends on MBD. It can help people in the medical profession to get comprehensive understanding on the state of the art of MBD. It also has reference values for the research and application of the MBD visualization methods.

Biomarkers are urgently needed for the diagnosis and monitoring of disease progression in Parkinson's disease. Both DJ-1 and alpha-synuclein, two proteins critically involved in Parkinson's disease pathogenesis, have been tested as disease biomarkers in several recent studies with inconsistent results. These have been largely due to variation in the protein species detected by different antibodies, limited numbers of patients in some studies, or inadequate control of several important variables. In this study, the nature of DJ-1 and alpha-synuclein in human cerebrospinal fluid was studied by a combination of western blotting, gel filtration and mass spectrometry. Sensitive and quantitative Luminex assays detecting most, if not all, species of DJ-1 and alpha-synuclein in human cerebrospinal fluid were established. Cerebrospinal fluid concentrations of DJ-1 and alpha-synuclein from 117 patients with Parkinson's disease, 132 healthy individuals and 50 patients with Alzheimer's disease were analysed using newly developed, highly sensitive Luminex technology while controlling for several major confounders. A total of 299 individuals and 389 samples were analysed. The results showed that cerebrospinal fluid DJ-1 and alpha-synuclein levels were dependent on age and influenced by the extent of blood contamination in cerebrospinal fluid. Both DJ-1 and alpha-synuclein levels were decreased in Parkinson's patients versus controls or Alzheimer's patients when blood contamination was controlled for. In the population aged > or = 65 years, when cut-off values of 40 and 0.5 ng/ml were chosen for DJ-1 and alpha-synuclein, respectively, the sensitivity and specificity for patients with Parkinson's disease versus controls were 90 and 70% for DJ-1, and 92 and 58% for alpha-synuclein. A combination of the two markers did not enhance the test performance. There was no association between DJ-1 or alpha-synuclein and the severity of Parkinson's disease. Taken together, this represents the largest scale study for DJ-1 or alpha-synuclein in human cerebrospinal fluid so far, while using newly established sensitive Luminex assays, with controls for multiple variables. We have demonstrated that total DJ-1 and alpha-synuclein in human cerebrospinal fluid are helpful diagnostic markers for Parkinson's disease, if variables such as blood contamination and age are taken into consideration.

BACKGROUND: The first case of 2009 pandemic influenza A (H1N1) virus infection in China was documented on May 10. Subsequently, persons with suspected cases of infection and contacts of those with suspected infection were tested. Persons in whom infection was confirmed were hospitalized and quarantined, and some of them were closely observed for the purpose of investigating the nature and duration of the disease. METHODS: During May and June 2009, we observed 426 persons infected with the 2009 pandemic influenza A (H1N1) virus who were quarantined in 61 hospitals in 20 provinces. Real-time reverse-transcriptase-polymerase-chain-reaction (RT-PCR) testing was used to confirm infection, the clinical features of the disease were closely monitored, and 254 patients were treated with oseltamivir within 48 hours after the onset of disease. RESULTS: The mean age of the 426 patients was 23.4 years, and 53.8% were male. The diagnosis was made at ports of entry (in 32.9% of the patients), during quarantine (20.2%), and in the hospital (46.9%). The median incubation period of the virus was 2 days (range, 1 to 7). The most common symptoms were fever (in 67.4% of the patients) and cough (69.5%). The incidence of diarrhea was 2.8%, and the incidence of nausea and vomiting was 1.9%. Lymphopenia, which was common in both adults (68.1%) and children (92.3%), typically occurred on day 2 (range, 1 to 3) and resolved by day 7 (range, 6 to 9). Hypokalemia was observed in 25.4% of the patients. Duration of fever was typically 3 days (range, 1 to 11). The median length of time during which patients had positive real-time RT-PCR test results was 6 days (range, 1 to 17). Independent risk factors for prolonged real-time RT-PCR positivity included an age of less than 14 years, male sex, and a delay from the onset of symptoms to treatment with oseltamivir of more than 48 hours. CONCLUSIONS: Surveillance of the 2009 H1N1 virus in China shows that the majority of those infected have a mild illness. The typical period during which the virus can be detected with the use of real-time RT-PCR is 6 days (whether or not fever is present). The duration of infection may be shortened if oseltamivir is administered.

Background In recent years, since the molecular docking technique can greatly improve the efficiency and reduce the research cost, it has become a key tool in computer‐assisted drug design to predict the binding affinity and analyze the interactive mode. Results This study introduces the key principles, procedures and the widely‐used applications for molecular docking. Also, it compares the commonly used docking applications and recommends which research areas are suitable for them. Lastly, it briefly reviews the latest progress in molecular docking such as the integrated method and deep learning. Conclusion Limited to the incomplete molecular structure and the shortcomings of the scoring function, current docking applications are not accurate enough to predict the binding affinity. However, we could improve the current molecular docking technique by integrating the big biological data into scoring function.

Hepatocellular carcinoma (HCC) is the most frequent subtype of primary liver cancer and one of the leading causes of cancer-related death worldwide. However, the molecular mechanisms underlying HCC pathogenesis have not been fully understood. Emerging evidences have recently suggested the crucial role of long noncoding RNAs (lncRNAs) in the tumorigenesis and progression of HCC. Various HCC-related lncRNAs have been shown to possess aberrant expression and participate in cancerous phenotypes (e.g. persistent proliferation, evading apoptosis, accelerated vessel formation and gain of invasive capability) through their binding with DNA, RNA or proteins, or encoding small peptides. Thus, a deeper understanding of lncRNA dysregulation would provide new insights into HCC pathogenesis and novel tools for the early diagnosis and treatment of HCC. In this review, we summarize the dysregulation of lncRNAs expression in HCC and their tumor suppressive or oncogenic roles during HCC tumorigenesis. Moreover, we discuss the diagnostic and therapeutic potentials of lncRNAs in HCC.

AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind that can predict three-dimensional (3D) structures of proteins from amino acid sequences with atomic-level accuracy. Protein structure prediction is one of the most challenging problems in computational biology and chemistry, and has puzzled scientists for 50 years. The advent of AF2 presents an unprecedented progress in protein structure prediction and has attracted much attention. Subsequent release of structures of more than 200 million proteins predicted by AF2 further aroused great enthusiasm in the science community, especially in the fields of biology and medicine. AF2 is thought to have a significant impact on structural biology and research areas that need protein structure information, such as drug discovery, protein design, prediction of protein function, et al. Though the time is not long since AF2 was developed, there are already quite a few application studies of AF2 in the fields of biology and medicine, with many of them having preliminarily proved the potential of AF2. To better understand AF2 and promote its applications, we will in this article summarize the principle and system architecture of AF2 as well as the recipe of its success, and particularly focus on reviewing its applications in the fields of biology and medicine. Limitations of current AF2 prediction will also be discussed.

ChatGPT is receiving increasing attention and has a variety of application scenarios in clinical practice. In clinical decision support, ChatGPT has been used to generate accurate differential diagnosis lists, support clinical decision-making, optimize clinical decision support, and provide insights for cancer screening decisions. In addition, ChatGPT has been used for intelligent question-answering to provide reliable information about diseases and medical queries. In terms of medical documentation, ChatGPT has proven effective in generating patient clinical letters, radiology reports, medical notes, and discharge summaries, improving efficiency and accuracy for health care providers. Future research directions include real-time monitoring and predictive analytics, precision medicine and personalized treatment, the role of ChatGPT in telemedicine and remote health care, and integration with existing health care systems. Overall, ChatGPT is a valuable tool that complements the expertise of health care providers and improves clinical decision-making and patient care. However, ChatGPT is a double-edged sword. We need to carefully consider and study the benefits and potential dangers of ChatGPT. In this viewpoint, we discuss recent advances in ChatGPT research in clinical practice and suggest possible risks and challenges of using ChatGPT in clinical practice. It will help guide and support future artificial intelligence research similar to ChatGPT in health.

ChatGPT has promising applications in health care, but potential ethical issues need to be addressed proactively to prevent harm. ChatGPT presents potential ethical challenges from legal, humanistic, algorithmic, and informational perspectives. Legal ethics concerns arise from the unclear allocation of responsibility when patient harm occurs and from potential breaches of patient privacy due to data collection. Clear rules and legal boundaries are needed to properly allocate liability and protect users. Humanistic ethics concerns arise from the potential disruption of the physician-patient relationship, humanistic care, and issues of integrity. Overreliance on artificial intelligence (AI) can undermine compassion and erode trust. Transparency and disclosure of AI-generated content are critical to maintaining integrity. Algorithmic ethics raise concerns about algorithmic bias, responsibility, transparency and explainability, as well as validation and evaluation. Information ethics include data bias, validity, and effectiveness. Biased training data can lead to biased output, and overreliance on ChatGPT can reduce patient adherence and encourage self-diagnosis. Ensuring the accuracy, reliability, and validity of ChatGPT-generated content requires rigorous validation and ongoing updates based on clinical practice. To navigate the evolving ethical landscape of AI, AI in health care must adhere to the strictest ethical standards. Through comprehensive ethical guidelines, health care professionals can ensure the responsible use of ChatGPT, promote accurate and reliable information exchange, protect patient privacy, and empower patients to make informed decisions about their health care.

Solar carbon dioxide (CO2) conversion is an emerging solution to meet the challenges of sustainable energy systems and environmental/climate concerns. However, the construction of isolated active sites not only influences catalytic activity but also limits the understanding of the structure–catalyst relationship of CO2 reduction. Herein, we develop a universal synthetic protocol to fabricate different single-atom metal sites (e.g., Fe, Co, Ni, Zn, Cu, Mn, and Ru) anchored on the triazine-based covalent organic framework (SAS/Tr-COF) backbone with the bridging structure of metal–nitrogen–chlorine for high-performance catalytic CO2 reduction. Remarkably, the as-synthesized Fe SAS/Tr-COF as a representative catalyst achieved an impressive CO generation rate as high as 980.3 μmol g–1 h–1 and a selectivity of 96.4%, over approximately 26 times higher than that of the pristine Tr-COF under visible light irradiation. From X-ray absorption fine structure analysis and density functional theory calculations, the superior photocatalytic performance is attributed to the synergic effect of atomically dispersed metal sites and Tr-COF host, decreasing the reaction energy barriers for the formation of *COOH intermediates and promoting CO2 adsorption and activation as well as CO desorption. This work not only affords rational design of state-of-the-art catalysts at the molecular level but also provides in-depth insights for efficient CO2 conversion.

Sebastien Gagneux and colleagues analyze a global collection of Mycobacterium tuberculosis clinical isolates to classify sublineages by phylogeography. They find globally distributed ‘generalist’ and geographically restricted ‘specialist’ sublineages of lineage 4, indicating that different evolutionary strategies were adopted to succeed in various ecological niches. Generalist and specialist species differ in the breadth of their ecological niches. Little is known about the niche width of obligate human pathogens. Here we analyzed a global collection of Mycobacterium tuberculosis lineage 4 clinical isolates, the most geographically widespread cause of human tuberculosis. We show that lineage 4 comprises globally distributed and geographically restricted sublineages, suggesting a distinction between generalists and specialists. Population genomic analyses showed that, whereas the majority of human T cell epitopes were conserved in all sublineages, the proportion of variable epitopes was higher in generalists. Our data further support a European origin for the most common generalist sublineage. Hence, the global success of lineage 4 reflects distinct strategies adopted by different sublineages and the influence of human migration.

Segmentation of pneumonia lesions from CT scans of COVID-19 patients is important for accurate diagnosis and follow-up. Deep learning has a potential to automate this task but requires a large set of high-quality annotations that are difficult to collect. Learning from noisy training labels that are easier to obtain has a potential to alleviate this problem. To this end, we propose a novel noise-robust framework to learn from noisy labels for the segmentation task. We first introduce a noise-robust Dice loss that is a generalization of Dice loss for segmentation and Mean Absolute Error (MAE) loss for robustness against noise, then propose a novel COVID-19 Pneumonia Lesion segmentation network (COPLE-Net) to better deal with the lesions with various scales and appearances. The noise-robust Dice loss and COPLE-Net are combined with an adaptive self-ensembling framework for training, where an Exponential Moving Average (EMA) of a student model is used as a teacher model that is adaptively updated by suppressing the contribution of the student to EMA when the student has a large training loss. The student model is also adaptive by learning from the teacher only when the teacher outperforms the student. Experimental results showed that: (1) our noise-robust Dice loss outperforms existing noise-robust loss functions, (2) the proposed COPLE-Net achieves higher performance than state-of-the-art image segmentation networks, and (3) our framework with adaptive self-ensembling significantly outperforms a standard training process and surpasses other noise-robust training approaches in the scenario of learning from noisy labels for COVID-19 pneumonia lesion segmentation.

Cancer remains a significant risk to human health. Nanomedicine is a new multidisciplinary field that is garnering a lot of interest and investigation. Nanomedicine shows great potential for cancer diagnosis and treatment. Specifically engineered nanoparticles can be employed as contrast agents in cancer diagnostics to enable high sensitivity and high-resolution tumor detection by imaging examinations. Novel approaches for tumor labeling and detection are also made possible by the use of nanoprobes and nanobiosensors. The achievement of targeted medication delivery in cancer therapy can be accomplished through the rational design and manufacture of nanodrug carriers. Nanoparticles have the capability to effectively transport medications or gene fragments to tumor tissues via passive or active targeting processes, thus enhancing treatment outcomes while minimizing harm to healthy tissues. Simultaneously, nanoparticles can be employed in the context of radiation sensitization and photothermal therapy to enhance the therapeutic efficacy of malignant tumors. This review presents a literature overview and summary of how nanotechnology is used in the diagnosis and treatment of malignant tumors. According to oncological diseases originating from different systems of the body and combining the pathophysiological features of cancers at different sites, we review the most recent developments in nanotechnology applications. Finally, we briefly discuss the prospects and challenges of nanotechnology in cancer.

Endoplasmic reticulum (ER) is a dynamic organelle orchestrating the folding and post-translational maturation of almost all membrane proteins and most secreted proteins. These proteins synthesized in the ER, need to form disulfide bridge to acquire specific three-dimensional structures for function. The formation of disulfide bridge is mediated via protein disulfide isomerase (PDI) family and other oxidoreductases, which contribute to reactive oxygen species (ROS) generation and consumption in the ER. Therefore, redox regulation of ER is delicate and sensitive to perturbation. Deregulation in ER homeostasis, usually called ER stress, can provoke unfolded protein response (UPR) pathways with an aim to initially restore homeostasis by activating genes involved in protein folding and antioxidative machinery. Over time, however, activated UPR involves a variety of cellular signaling pathways which determine the state and fate of cell in large part (like autophagy, apoptosis, ferroptosis, inflammation, senescence, stemness, and cell cycle, etc.). This review will describe the regulation of UPR from the redox perspective in controlling the cell survival or death, emphasizing the redox modifications of UPR sensors/transducers in the ER.

Importance: Overweight and obesity in childhood and adolescence is a global health issue associated with adverse outcomes throughout the life course. Objective: To estimate worldwide prevalence of overweight and obesity in children and adolescents from 2000 to 2023 and to assess potential risk factors for and comorbidities of obesity. Data Sources: MEDLINE, Web of Science, Embase, and Cochrane. Study Selection: The inclusion criteria were: (1) studies provided adequate information, (2) diagnosis based on body mass index cutoffs proposed by accepted references, (3) studies performed on general population between January 2000 and March 2023, (4) participants were younger than 18 years. Data Extraction and Synthesis: The current study was performed in accordance with the Meta-analysis of Observational Studies in Epidemiology guidelines. DerSimonian-Laird random-effects model with Free-Tukey double arcsine transformation was used for data analysis. Sensitivity analysis, meta-regression, and subgroup analysis of obesity among children and adolescents were conducted. Main Outcomes and Measures: Prevalence of overweight and obesity among children and adolescents assessed by World Health Organization, International Obesity Task Force, the US Centers for Disease Control and Prevention, or other national references. Results: A total of 2033 studies from 154 different countries or regions involving 45 890 555 individuals were included. The overall prevalence of obesity in children and adolescents was 8.5% (95% CI 8.2-8.8). We found that the prevalence varied across countries, ranging from 0.4% (Vanuatu) to 28.4% (Puerto Rico). Higher prevalence of obesity among children and adolescents was reported in countries with Human Development Index scores of 0.8 or greater and high-income countries or regions. Compared to 2000 to 2011, a 1.5-fold increase in the prevalence of obesity was observed in 2012 to 2023. Substantial differences in rates of obesity were noted when stratified by 11 risk factors. Children and adolescents with obesity had a high risk of depression and hypertension. The pooled estimates of overweight and excess weight in children and adolescents were 14.8% (95% CI 14.5-15.1) and 22.2% (95% CI 21.6-22.8), respectively. Conclusions and Relevance: This study's findings indicated 1 of 5 children or adolescents experienced excess weight and that rates of excess weight varied by regional income and Human Development Index. Excess weight among children and adolescents was associated with a mix of inherent, behavioral, environmental, and sociocultural influences that need the attention and committed intervention of primary care professionals, clinicians, health authorities, and the general public.