From bench to bedside: The promising value of exosomes in precision medicine for CNS tumors.

Exosomes are naturally present extracellular vesicles (EVs) released into the surrounding body fluids upon the fusion of polycystic and plasma membranes. They facilitate intercellular communication by transporting DNA, mRNA, microRNA, long non-coding RNA, circular RNA, proteins, lipids, and nucleic acids. They contribute to the onset and progression of Central Nervous System (CNS) tumors. In addition, they can be used as biomarkers of tumor proliferation, migration, and blood vessel formation, thereby affecting the Tumor Microenvironment (TME). This paper reviews the recent advancements in the diagnosis and treatment of exosomes in various CNS tumors, the promise and challenges of exosomes as natural carriers of CNS tumors, and the therapeutic prospects of exosomes in CNS tumors. Furthermore, we hope this research can contribute to the development of more targeted and effective treatments for central nervous system tumors.

Rapid diagnosis and precision treatment of Helicobacter pylori infection in clinical settings.

Helicobacter pylori is a gram-negative bacterium that colonizes the stomach of approximately half of the worldwide population, with higher prevalence in densely populated areas like Asia, the Caribbean, Latin America, and Africa. H. pylori infections range from asymptomatic cases to potentially fatal diseases, including peptic ulcers, chronic gastritis, and stomach adenocarcinoma. The management of these conditions has become more difficult due to the rising prevalence of drug-resistant H. pylori infections, which ultimately lead to gastric cancer and mucosa-associated lymphoid tissue (MALT) lymphoma. In 1994, the International Agency for Research on Cancer (IARC) categorized H. pylori as a Group I carcinogen, contributing to approximately 780,000 cancer cases annually. Antibiotic resistance against drugs used to treat H. pylori infections ranges between 15% and 50% worldwide, with Asian countries having exceptionally high rates. This review systematically examines the impacts of H. pylori infection, the increasing prevalence of antibiotic resistance, and the urgent need for accurate diagnosis and precision treatment. The present status of precision treatment strategies and prospective approaches for eradicating infections caused by antibiotic-resistant H. pylori will also be evaluated.

Genetic interactions reveal distinct biological and therapeutic implications in breast cancer.

Co-occurrence and mutual exclusivity of genomic alterations may reflect the existence of genetic interactions, potentially shaping distinct biological phenotypes and impacting therapeutic response in breast cancer. However, our understanding of them remains limited. Herein, we investigate a large-scale multi-omics cohort (n = 873) and a real-world clinical sequencing cohort (n = 4,405) including several clinical trials with detailed treatment outcomes and perform functional validation in patient-derived organoids, tumor fragments, and in vivo models. Through this comprehensive approach, we construct a network comprising co-alterations and mutually exclusive events and characterize their therapeutic potential and underlying biological basis. Notably, we identify associations between TP53mut-AURKAamp and endocrine therapy resistance, germline BRCA1mut-MYCamp and improved sensitivity to PARP inhibitors, and TP53mut-MYBamp and immunotherapy resistance. Furthermore, we reveal that precision treatment strategies informed by co-alterations hold promise to improve patient outcomes. Our study highlights the significance of genetic interactions in guiding genome-informed treatment decisions beyond single driver alterations.

Calcification-associated molecular traits and therapeutic strategies in hormone receptor-positive HER2-negative breast cancer.

OBJECTIVE: Mammographic calcifications are a common feature of breast cancer, but their molecular characteristics and treatment implications in hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) breast cancer remain unclear. METHODS: We retrospectively collected mammography records of an HR+/HER2- breast cancer cohort (n = 316) with matched clinicopathological, genomic, transcriptomic, and metabolomic data. On the basis of mammographic images, we grouped tumors by calcification status into calcification-negative tumors, tumors with probably benign calcifications, tumors with calcification of low-moderate suspicion for maligancy and tumors with calcification of high suspicion for maligancy. We then explored the molecular characteristics associated with each calcification status across multiple dimensions. RESULTS: Among the different statuses, tumors with probably benign calcifications exhibited elevated hormone receptor immunohistochemical staining scores, estrogen receptor (ER) pathway activation, lipid metabolism, and sensitivity to endocrine therapy. Tumors with calcifications of high suspicion for malignancy had relatively larger tumor sizes, elevated lymph node metastasis incidence, Ki-67 staining scores, genomic instability, cell cycle pathway activation, and may benefit from cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitors. CONCLUSIONS: Our research established links between tumor calcifications and molecular features, thus proposing potential precision treatment strategies for HR+/HER2- breast cancer.

Proteogenomic characterization of small cell lung cancer identifies biological insights and subtype-specific therapeutic strategies.

We performed comprehensive proteogenomic characterization of small cell lung cancer (SCLC) using paired tumors and adjacent lung tissues from 112 treatment-naive patients who underwent surgical resection. Integrated multi-omics analysis illustrated cancer biology downstream of genetic aberrations and highlighted oncogenic roles of FAT1 mutation, RB1 deletion, and chromosome 5q loss. Two prognostic biomarkers, HMGB3 and CASP10, were identified. Overexpression of HMGB3 promoted SCLC cell migration via transcriptional regulation of cell junction-related genes. Immune landscape characterization revealed an association between ZFHX3 mutation and high immune infiltration and underscored a potential immunosuppressive role of elevated DNA damage response activity via inhibition of the cGAS-STING pathway. Multi-omics clustering identified four subtypes with subtype-specific therapeutic vulnerabilities. Cell line and patient-derived xenograft-based drug tests validated the specific therapeutic responses predicted by multi-omics subtyping. This study provides a valuable resource as well as insights to better understand SCLC biology and improve clinical practice.

Recent Advances of Emerging Metal-Containing Two-Dimensional Nanomaterials in Tumor Theranostics.

In recent years, metal-containing two-dimensional (2D) nanomaterials, among various 2D nanomaterials have attracted widespread attention because of their unique physical and chemical properties, especially in the fields of biomedical applications. Firstly, the review provides a brief introduction to two types of metal-containing 2D nanomaterials, based on whether metal species take up the major skeleton of the 2D nanomaterials. After this, the synthetical approaches are summarized, focusing on two strategies similar to other 2D nanomaterials, top-down and bottom-up methods. Then, the performance and evaluation of these 2D nanomaterials when applied to cancer therapy are discussed in detail. The specificity of metal-containing 2D nanomaterials in physics and optics makes them capable of killing cancer cells in a variety of ways, such as photodynamic therapy, photothermal therapy, sonodynamic therapy, chemodynamic therapy and so on. Besides, the integrated platform of diagnosis and treatment and the clinical translatability through metal-containing 2D nanomaterials is also introduced in this review. In the summary and perspective section, advanced rational design, challenges and promising clinical contributions to cancer therapy of these emerging metal-containing 2D nanomaterials are discussed.

Deep learning in computed tomography to predict endotype in chronic rhinosinusitis with nasal polyps.

BACKGROUND: As treatment strategies differ according to endotype, rhinologists must accurately determine the endotype in patients affected by chronic rhinosinusitis with nasal polyps (CRSwNP) for the appropriate management. In this study, we aim to construct a novel deep learning model using paranasal sinus computed tomography (CT) to predict the endotype in patients with CRSwNP. METHODS: We included patients diagnosed with CRSwNP between January 1, 2020, and April 31, 2023. The endotype of patients with CRSwNP in this study was classified as eosinophilic or non-eosinophilic. Sinus CT images (29,993 images) were retrospectively collected, including the axial, coronal, and sagittal planes, and randomly divided into training, validation, and testing sets. A residual network-18 was used to construct the deep learning model based on these images. Loss functions, accuracy functions, confusion matrices, and receiver operating characteristic curves were used to assess the predictive performance of the model. Gradient-weighted class activation mapping was performed to visualize and interpret the operating principles of the model. RESULTS: Among 251 included patients, 86 and 165 had eosinophilic or non-eosinophilic CRSwNP, respectively. The median (interquartile range) patient age was 49 years (37-58 years), and 153 (61.0%) were male. The deep learning model showed good discriminative performance in the training and validation sets, with areas under the curves of 0.993 and 0.966, respectively. To confirm the model generalizability, the receiver operating characteristic curve in the testing set showed good discriminative performance, with an area under the curve of 0.963. The Kappa scores of the confusion matrices in the training, validation, and testing sets were 0.985, 0.928, and 0.922, respectively. Finally, the constructed deep learning model was used to predict the endotype of all patients, resulting in an area under the curve of 0.962. CONCLUSIONS: The deep learning model developed in this study may provide a novel noninvasive method for rhinologists to evaluate endotypes in patients with CRSwNP and help develop precise treatment strategies.

A precise prognostic signature in CTNNB1-mutant hepatocellular carcinoma: Prognosis prediction and precision treatment exploration.

Background: CTNNB1 mutates in most hepatocellular carcinoma (HCC) which is the most familiar form of liver cancer with high heterogeneity. It is critical to create a specific prognostication methodology and to investigate additional treatment options for CTNNB1-mutant HCCs. Methods: A total of 926 samples in five independent cohorts were enrolled in this study, including 127 CTNNB1-mutant samples and 75 estimated CTNNB1-mutant samples. The prognostic signature was constructed by LASSO-Cox regression and evaluated by bioinformatics analyses. The selection of possible drug targets and agents was produced based on the expression profiles and drug sensitivity data of cancer cell lines in two databases. Results: A prognostic signature based on 15 genes categorized the CTNNB1-mutant HCCs into two groups with different risks. Compared to low-risk patients, high-risk patients had significantly inferior prognoses. ROC curve and multivariate analysis also indicated the superior performance of our signature on the prognosis estimation, particularly in CTNNB1-mutant HCCs. Besides, the nomogram was constructed according to the prognostic signature with excellent predictive performance confirmed by the calibration curve. Subsequently, we suggested that AT-7519 and PHA-793887 might be potential drug agents for high-risk patients. Conclusion: We established a 15-gene prognostic model, particularly in HCCs with CTNNB1 mutations with good predictive efficiency. Besides, we explored the potential drug targets and agents for patients with high risk. Our findings offered a fresh idea for personalized prognosis management in HCCs with CTNNB1 mutations and threw new insight for precise treatment in HCCs as well.

Molecular classifications of prostate cancer: basis for individualized risk stratification and precision therapy.

Tumour classifications play a pivotal role in prostate cancer (PCa) management. It can predict the clinical outcomes of PCa as early as the disease is diagnosed and then guide therapeutic schemes, such as active monitoring, standalone surgical intervention, or surgery supplemented with postoperative adjunctive therapy, thereby circumventing disease exacerbation and excessive treatment. Classifications based on clinicopathological features, such as prostate cancer-specific antigen, Gleason score, and TNM stage, are still the main risk stratification strategies and have played an essential role in standardized clinical decision-making. However, mounting evidence indicates that clinicopathological parameters in isolation fail to adequately capture the heterogeneity exhibited among distinct PCa patients, such as those sharing identical Gleason scores yet experiencing divergent prognoses. As a remedy, molecular classifications have been introduced. Currently, molecular studies have revealed the characteristic genomic alterations, epigenetic modulations, and tumour microenvironment associated with different types of PCa, which provide a chance for urologists to refine the PCa classification. In this context, numerous invaluable molecular classifications have been devised, employing disparate statistical methodologies and algorithmic approaches, encompassing self-organizing map clustering, unsupervised cluster analysis, and multifarious algorithms. Interestingly, the classifier PAM50 was used in a phase-2 multicentre open-label trial, NRG-GU-006, for further validation, which hints at the promise of molecular classification for clinical use. Consequently, this review examines the extant molecular classifications, delineates the prevailing panorama of clinically pertinent molecular signatures, and delves into eight emblematic molecular classifications, dissecting their methodological underpinnings and clinical utility.

Network pharmacology: a bright guiding light on the way to explore the personalized precise medication of traditional Chinese medicine.

Network pharmacology can ascertain the therapeutic mechanism of drugs for treating diseases at the level of biological targets and pathways. The effective mechanism study of traditional Chinese medicine (TCM) characterized by multi-component, multi-targeted, and integrative efficacy, perfectly corresponds to the application of network pharmacology. Currently, network pharmacology has been widely utilized to clarify the mechanism of the physiological activity of TCM. In this review, we comprehensively summarize the application of network pharmacology in TCM to reveal its potential of verifying the phenotype and underlying causes of diseases, realizing the personalized and accurate application of TCM. We searched the literature using "TCM network pharmacology" and "network pharmacology" as keywords from Web of Science, PubMed, Google Scholar, as well as Chinese National Knowledge Infrastructure in the last decade. The origins, development, and application of network pharmacology are closely correlated with the study of TCM which has been applied in China for thousands of years. Network pharmacology and TCM have the same core idea and promote each other. A well-defined research strategy for network pharmacology has been utilized in several aspects of TCM research, including the elucidation of the biological basis of diseases and syndromes, the prediction of TCM targets, the screening of TCM active compounds, and the decipherment of mechanisms of TCM in treating diseases. However, several factors limit its application, such as the selection of databases and algorithms, the unstable quality of the research results, and the lack of standardization. This review aims to provide references and ideas for the research of TCM and to encourage the personalized and precise use of Chinese medicine.

Genomic medicine to reduce tobacco and related disorders: Translation to precision prevention and treatment.

Genomic medicine can enhance prevention and treatment. First, we propose that advances in genomics have the potential to enhance assessment of disease risk, improve prognostic predictions, and guide treatment development and application. Clinical implementation of polygenic risk scores (PRSs) has emerged as an area of active research. The pathway from genomic discovery to implementation is an iterative process. Second, we provide examples on how genomic medicine has the potential to solve problems in prevention and treatment using two examples: Lung cancer screening and evidence-based tobacco treatment are both under-utilized and great opportunities for genomic interventions. Third, we discuss the translational process for developing genomic interventions from evidence to implementation by presenting a model to evaluate genomic evidence for clinical implementation, mechanisms of genomic interventions, and patient desire for genomic interventions. Fourth, we present potential challenges in genomic interventions including a great need for evidence in all diverse populations, little evidence on treatment algorithms, challenges in accommodating a dynamic evidence base, and implementation challenges in real world clinical settings. Finally, we conclude that research to identify genomic markers that are associated with smoking cessation success and the efficacy of smoking cessation treatments is needed to empower people of all diverse ancestry. Importantly, genomic data can be used to help identify patients with elevated risk for nicotine addiction, difficulty quitting smoking, favorable response to specific pharmacotherapy, and tobacco-related health problems.

Exosomes: Potential key players towards novel therapeutic options in diabetic wounds.

Diabetic wounds are usually difficult to heal, and wounds in foot in particular are often aggravated by infection, trauma, diabetic neuropathy, peripheral vascular disease and other factors, resulting in serious foot ulcers. The pathogenesis and clinical manifestations of diabetic wounds are complicated, and there is still a lack of objective and in-depth laboratory diagnosis and classification standards. Exosomes are nanoscale vesicles containing DNA, mRNA, microRNA, cyclic RNA, metabolites, lipids, cytoplasm and cell surface proteins, etc., which are involved in intercellular communication and play a crucial role in vascular regeneration, tissue repair and inflammation regulation in the process of diabetic wound healing. Here, we discussed exosomes of different cellular origins, such as diabetic wound-related fibroblasts (DWAF), adipose stem cells (ASCs), mesenchymal stem cells (MSCs), immune cells, platelets, human amniotic epithelial cells (hAECs), epidermal stem cells (ESCs), and their various molecular components. They exhibit multiple therapeutic effects during diabetic wound healing, including promoting cell proliferation and migration associated with wound healing, regulating macrophage polarization to inhibit inflammatory responses, promoting nerve repair, and promoting vascular renewal and accelerating wound vascularization. In addition, exosomes can be designed to deliver different therapeutic loads and have the ability to deliver them to the desired target. Therefore, exosomes may become an innovative target for precision therapeutics in diabetic wounds. In this review, we summarize the latest research on the role of exosomes in the healing of diabetic wound by regulating the pathogenesis of diabetic wounds, and discuss their potential applications in the precision treatment of diabetic wounds.

Development of CRISPR Cas9, spin-off technologies and their application in model construction and potential therapeutic methods of Parkinson's disease.

Parkinson's disease (PD) is one of the most common degenerative diseases. It is most typically characterized by neuronal death following the accumulation of Lewis inclusions in dopaminergic neurons in the substantia nigra region, with clinical symptoms such as motor retardation, autonomic dysfunction, and dystonia spasms. The exact molecular mechanism of its pathogenesis has not been revealed up to now. And there is a lack of effective treatments for PD, which places a burden on patients, families, and society. CRISPR Cas9 is a powerful technology to modify target genomic sequence with rapid development. More and more scientists utilized this technique to perform research associated neurodegenerative disease including PD. However, the complexity involved makes it urgent to organize and summarize the existing findings to facilitate a clearer understanding. In this review, we described the development of CRISPR Cas9 technology and the latest spin-off gene editing systems. Then we focused on the application of CRISPR Cas9 technology in PD research, summarizing the construction of the novel PD-related medical models including cellular models, small animal models, large mammal models. We also discussed new directions and target molecules related to the use of CRISPR Cas9 for PD treatment from the above models. Finally, we proposed the view about the directions for the development and optimization of the CRISPR Cas9 technology system, and its application to PD and gene therapy in the future. All these results provided a valuable reference and enhanced in understanding for studying PD.

L. pneumophila Infection Diagnosed by tNGS in a Lady with Lymphadenopathy.

We report a case of a 34-year-old lady with multiple joint pain. Autoimmune diseases were considered first with a positive result of anti-Ro antibody and her right knee joint cavity effusion. Later, bilateral interstitial changes in her lungs and mediastinal lymphadenopathy were found after chest CT scanning. Empirical quinolone therapy was given although pathological examinations of blood, sputum and bronchoalveolar lavage fluid (BALF) did not find anything. Finally, Legionella pneumophila was identified by target next-generation sequencing (tNGS) detection. This case highlighted the timely use of tNGS, a new tool with fast speed, high accuracy and effective cost, could help to identify atypical infection and start an early therapy.

Early recognition of characteristic conventional and amplitude-integrated EEG patterns of seizures in SCN2A and KCNQ3-related epilepsy in neonates.

PURPOSE: Early recognition of seizures in neonates secondary to pathogenic variants in potassium or sodium channel coding genes is crucial, as these seizures are often resistant to commonly used anti-seizure medications but respond well to sodium channel blockers. Recently, a characteristic ictal amplitude-integrated electroencephalogram (aEEG) pattern was described in neonates with KCNQ2-related epilepsy. We report a similar aEEG pattern in seizures caused by SCN2A- and KCNQ3-pathogenic variants, as well as conventional EEG (cEEG) descriptions. METHODS: International multicentre descriptive study, reporting clinical characteristics, aEEG and cEEG findings of 13 neonates with seizures due to pathogenic SCN2A- and KCNQ3-variants. As a comparison group, aEEGs and cEEGs of neonates with seizures due to hypoxic-ischemic encephalopathy (n = 117) and other confirmed genetic causes affecting channel function (n = 55) were reviewed. RESULTS: In 12 out of 13 patients, the aEEG showed a characteristic sequence of brief onset with a decrease, followed by a quick rise, and then postictal amplitude attenuation. This pattern correlated with bilateral EEG onset attenuation, followed by rhythmic discharges ending in several seconds of post-ictal amplitude suppression. Apart from patients with KCNQ2-related epilepsy, none of the patients in the comparison groups had a similar aEEG or cEEG pattern. DISCUSSION: Seizures in SCN2A- and KCNQ3-related epilepsy in neonates can usually be recognized by a characteristic ictal aEEG pattern, previously reported only in KCNQ2-related epilepsy, extending this unique feature to other channelopathies. Awareness of this pattern facilitates the prompt initiation of precision treatment with sodium channel blockers even before genetic results are available.

Unraveling Colorectal Cancer and Pan-cancer Immune Heterogeneity and Synthetic Therapy Response Using Cuproptosis and Hypoxia Regulators by Multi-omic Analysis and Experimental Validation.

Cuproptosis, a new type of programmed cell death (PCD), is closely related to cellular tricarboxylic acid cycle and cellular respiration, while hypoxia can modulate PCD. However, their combined contribution to tumor subtyping remains unexplored. Here, we applied a multi-omics approach to classify TCGA_COADREAD based on cuproptosis and hypoxia. The classification was validated in three colorectal cancer (CRC) cohorts and extended to a pan-cancer analysis. The results demonstrated that pan-cancers, including CRC, could be divided into three distinct subgroups (cuproptosis-hypoxia subtypes, CHSs): CHS1 had active metabolism and poor immune infiltration but low fibrosis; CHS3 had contrasting characteristics with CHS1; CHS2 was intermediate. CHS1 may respond well to cuproptosis inducers, and CHS3 may benefit from a combination of immunotherapy and anti-fibrosis/anti-hypoxia therapies. In CRC, the CHSs also showed a significant difference in prognosis and sensitivity to classic drugs. Organoid-based drug sensitivity assays validated the results of transcriptomics. Cell-based assays indicated that masitinib and simvastatin had specific effects on CHS1 and CHS3, respectively. A user-friendly website based on the classifier was developed (https://fan-app.shinyapps.io/chs_classifier/) for accessibility. Overall, the classifier based on cuproptosis and hypoxia was applicable to most pan-cancers and could aid in personalized cancer therapy.

The use of biomarkers to guide precision treatment for tobacco use.

This review summarizes the evidence to date on the development of biomarkers for personalizing the pharmacological treatment of combustible tobacco use. First, the latest evidence on FDA-approved medications is considered, demonstrating that, while these medications offer real benefits, they do not contribute to smoking cessation in approximately two-thirds of cases. Second, the case for using biomarkers to guide tobacco treatment is made based on the potential to increase medication effectiveness and uptake and reduce side effects. Next, the FDA framework of biomarker development is presented along with the state of science on biomarkers for tobacco treatment, including a review of the nicotine metabolite ratio, electroencephalographic event-related potentials, and other biomarkers utilized for risk feedback. We conclude with a discussion of the challenges and opportunities for the translation of biomarkers to guide tobacco treatment and propose priorities for future research.

Practical Understanding of Cancer Model Identifiability in Clinical Applications.

Mathematical models are a core component in the foundation of cancer theory and have been developed as clinical tools in precision medicine. Modeling studies for clinical applications often assume an individual's characteristics can be represented as parameters in a model and are used to explain, predict, and optimize treatment outcomes. However, this approach relies on the identifiability of the underlying mathematical models. In this study, we build on the framework of an observing-system simulation experiment to study the identifiability of several models of cancer growth, focusing on the prognostic parameters of each model. Our results demonstrate that the frequency of data collection, the types of data, such as cancer proxy, and the accuracy of measurements all play crucial roles in determining the identifiability of the model. We also found that highly accurate data can allow for reasonably accurate estimates of some parameters, which may be the key to achieving model identifiability in practice. As more complex models required more data for identification, our results support the idea of using models with a clear mechanism that tracks disease progression in clinical settings. For such a model, the subset of model parameters associated with disease progression naturally minimizes the required data for model identifiability.

A novel strategy for precise prognosis management and treatment option in colon adenocarcinoma with TP53 mutations.

Background: TP53 is one of the most frequent mutated genes in colon cancer. Although colon cancer with TP53 mutations has a high risk of metastasis and worse prognosis generally, it showed high heterogeneity clinically. Methods: A total of 1,412 colon adenocarcinoma (COAD) samples were obtained from two RNA-seq cohorts and three microarray cohorts, including the TCGA-COAD (N = 408), the CPTAC-COAD (N = 106), GSE39582 (N = 541), GSE17536 (N = 171) and GSE41258 (N = 186). The LASSO-Cox method was used to establish the prognostic signature based on the expression data. The patients were divided into high-risk and low-risk groups based on the median risk score. The efficiency of the prognostic signature was validated in various cohorts, including TP53-mutant and TP53 wild-type. The exploration of potential therapeutic targets and agents was performed by using the expression data of TP53-mutant COAD cell lines obtained from the CCLE database and the corresponding drug sensitivity data obtained from the GDSC database. Results: A 16-gene prognostic signature was established in TP53-mutant COAD. The high-risk group had significantly inferior survival time compared to the low-risk group in all TP53-mutant datasets, while the prognostic signature failed to classify the prognosis of COAD with TP53 wild-type properly. Besides, the risk score was the independent poor factor for the prognosis in TP53-mutant COAD and the nomogram based on the risk score was also shown good predictive efficiency in TP53-mutant COAD. Moreover, we identified SGPP1, RHOQ, and PDGFRB as potential targets for TP53-mutant COAD, and illuminated that the high-risk patients might benefit from IGFR-3801, Staurosporine, and Sabutoclax. Conclusion: A novel prognostic signature with great efficiency was established especially for COAD patients with TP53 mutations. Besides, we identified novel therapeutic targets and potential sensitive agents for TP53-mutant COAD with high risk. Our findings provided not only a new strategy for prognosis management but also new clues for drug application and precision treatment in COAD with TP53 mutations.

[Future perspectives in surgery from a German point of view]. / Zukunftsperspektive in der Chirurgie aus deutscher Sicht.

In this article two aspects of the topic of future perspectives in surgery from a German point of view are discussed: firstly, topics of healthcare policy, such as upcoming alterations of the healthcare system, including required minimum quantities with subsequent centralization and the increasing diversity of our population with chances and challenges for surgery. Secondly, comprehensive personalized precision treatment, individualized organ transplantation and visionary developments in medical technology with artificial intelligence.