Intelligent medicine in focus: the 5 stages of evolution in robot-assisted surgery for prostate cancer in the past 20 years and future implications

Robot-assisted surgery has evolved into a crucial treatment for prostate cancer (PCa). However, from its appearance to today, brain-computer interface, virtual reality, and metaverse have revolutionized the field of robot-assisted surgery for PCa, presenting both opportunities and challenges. Especially in the context of contemporary big data and precision medicine, facing the heterogeneity of PCa and the complexity of clinical problems, it still needs to be continuously upgraded and improved. Keeping this in mind, this article summarized the 5 stages of the historical development of robot-assisted surgery for PCa, encompassing the stages of emergence, promotion, development, maturity, and intelligence. Initially, safety concerns were paramount, but subsequent research and engineering advancements have focused on enhancing device efficacy, surgical technology, and achieving precise multi modal treatment. The dominance of da Vinci robot-assisted surgical system has seen this evolution intimately tied to its successive versions. In the future, robot-assisted surgery for PCa will move towards intelligence, promising improved patient outcomes and personalized therapy, alongside formidable challenges. To guide future development, we propose 10 significant prospects spanning clinical, research, engineering, materials, social, and economic domains, envisioning a future era of artificial intelligence in the surgical treatment of PCa.

Toxoplasma gondii infection supports the infiltration of T cells into brain tumors

Few T cells infiltrate into primary brain tumors, fundamentally hampering the effectiveness of immunotherapy. We hypothesized that Toxoplasma gondii, a microorganism that naturally elicits a Th1 response in the brain, can promote T cell infiltration into brain tumors despite their immune suppressive microenvironment. Using a mouse genetic model for medulloblastoma, we found that T. gondii infection induced the infiltration of activatable T cells into the tumor mass and led to myeloid cell reprogramming toward a T cell-supportive state, without causing severe health issues in mice. The study provides a concrete foundation for future studies to take advantage of the immune modulatory capacity of T. gondii to facilitate brain tumor immunotherapy.

Multivariate analysis of metabolic state vulnerabilities across diverse cancer contexts reveals synthetically lethal associations

Targeting the distinct metabolic needs of tumor cells has recently emerged as a promising strategy for cancer therapy. The heterogeneous, context-dependent nature of cancer cell metabolism, however, poses challenges in identifying effective therapeutic interventions. Here, we utilize various unsupervised and supervised multivariate modeling approaches to systematically pinpoint recurrent metabolic states within hundreds of cancer cell lines, elucidate their association with tumor lineage and growth environments, and uncover vulnerabilities linked to their metabolic states across diverse genetic and tissue contexts. We validate key findings via analysis of data from patient-derived tumors and pharmacological screens, and by performing new genetic and pharmacological experiments. Our analysis uncovers new synthetically lethal associations between the tumor metabolic state (e.g., oxidative phosphorylation), driver mutations (e.g., loss of tumor suppressor PTEN), and actionable biological targets (e.g., mitochondrial electron transport chain). Investigating the mechanisms underlying these relationships can inform the development of more precise and context-specific, metabolism-targeted cancer therapies.

Exploring the potential of artificial intelligence to enhance the writing of english academic papers by non-native english-speaking medical students - the educational application of ChatGPT

BACKGROUND

Academic paper writing holds significant importance in the education of medical students, and poses a clear challenge for those whose first language is not English. This study aims to investigate the effectiveness of employing large language models, particularly ChatGPT, in improving the English academic writing skills of these students.

METHODS

A cohort of 25 third-year medical students from China was recruited. The study consisted of two stages. Firstly, the students were asked to write a mini paper. Secondly, the students were asked to revise the mini paper using ChatGPT within two weeks. The evaluation of the mini papers focused on three key dimensions, including structure, logic, and language. The evaluation method incorporated both manual scoring and AI scoring utilizing the ChatGPT-3.5 and ChatGPT-4 models. Additionally, we employed a questionnaire to gather feedback on students' experience in using ChatGPT.

RESULTS

After implementing ChatGPT for writing assistance, there was a notable increase in manual scoring by 4.23 points. Similarly, AI scoring based on the ChatGPT-3.5 model showed an increase of 4.82 points, while the ChatGPT-4 model showed an increase of 3.84 points. These results highlight the potential of large language models in supporting academic writing. Statistical analysis revealed no significant difference between manual scoring and ChatGPT-4 scoring, indicating the potential of ChatGPT-4 to assist teachers in the grading process. Feedback from the questionnaire indicated a generally positive response from students, with 92% acknowledging an improvement in the quality of their writing, 84% noting advancements in their language skills, and 76% recognizing the contribution of ChatGPT in supporting academic research.

CONCLUSION

The study highlighted the efficacy of large language models like ChatGPT in augmenting the English academic writing proficiency of non-native speakers in medical education. Furthermore, it illustrated the potential of these models to make a contribution to the educational evaluation process, particularly in environments where English is not the primary language.

Protocol for integrating tissue clearing and light-sheet imaging to analyze cancer initiation in mosaic analysis with double markers mouse models

Mosaic analysis with double markers (MADM) mouse models closely mimic the clonal origin of human cancers by generating sporadic, GFP-labeled cancer-initiating cells. Traditional clonal analysis pipelines are labor intensive, hindering throughput and disrupting the 3D architecture. Here, we present a protocol that integrates tissue clearing and light-sheet imaging to analyze pre-malignant clones in whole-mount MADM-labeled tissues. We describe steps for generating mosaic-labeled cancer mouse models, tissue harvesting, fixation, and clearing. We then detail procedures for light-sheet imaging and clonal size analysis. For complete details on the use and execution of this protocol, please refer to Zeng et al.1,2.

RARPKB: a knowledge-guide decision support platform for personalized robot-assisted surgery in prostate cancer

BACKGROUND

Robot-assisted radical prostatectomy (RARP) has emerged as a pivotal surgical intervention for the treatment of prostate cancer (PCa). However, the complexity of clinical cases, heterogeneity of PCa, and limitations in physician expertise pose challenges to rational decision-making in RARP. To address these challenges, the authors aimed to organize the knowledge of previously complex cohorts and establish an online platform named the RARP knowledge base (RARPKB) to provide reference evidence for personalized treatment plans.

MATERIALS AND METHODS

PubMed searches over the past two decades were conducted to identify publications describing RARP. The authors collected, classified, and structured surgical details, patient information, surgical data, and various statistical results from the literature. A knowledge-guided decision-support tool was established using MySQL, DataTable, ECharts, and JavaScript. ChatGPT-4 and two assessment scales were used to validate and compare the platform.

RESULTS

The platform comprised 583 studies, 1589 cohorts, 1 911 968 patients, and 11 986 records, resulting in 54 834 data entries. The knowledge-guided decision support tool provide personalized surgical plan recommendations and potential complications on the basis of patients' baseline and surgical information. Compared with ChatGPT-4, RARPKB outperformed in authenticity (100% vs. 73%), matching (100% vs. 53%), personalized recommendations (100% vs. 20%), matching of patients (100% vs. 0%), and personalized recommendations for complications (100% vs. 20%). Postuse, the average System Usability Scale score was 88.88±15.03, and the Net Promoter Score of RARPKB was 85. The knowledge base is available at: http://rarpkb.bioinf.org.cn .

CONCLUSIONS

The authors introduced the pioneering RARPKB, the first knowledge base for robot-assisted surgery, with an emphasis on PCa. RARPKB can assist in personalized and complex surgical planning for PCa to improve its efficacy. RARPKB provides a reference for the future applications of artificial intelligence in clinical practice.

The effect of rehabilitation time on functional recovery after arthroscopic rotator cuff repair: a systematic review and meta-analysis

Objective

We compared the effects of early and delayed rehabilitation on the function of patients after rotator cuff repair by meta-analysis to find effective interventions to promote the recovery of shoulder function.

Methods

This meta-analysis was registered in PROSPERO (CRD42023466122). We manually searched the randomized controlled trials (RCTs) in the Cochrane Library, Pubmed, Cochrane Library, EMBASE, the China National Knowledge Infrastructure (CNKI), the China VIP Database (VIP), and the Wanfang Database to evaluate the effect of early and delayed rehabilitation after arthroscopic shoulder cuff surgery on the recovery of shoulder joint function. Review Manager 5.3 software was used to analyze the extracted data. Then, the PEDro scale was employed to appraise the methodological quality of the included research.

Results

This research comprised nine RCTs and 830 patients with rotator cuff injuries. According to the findings of the meta-analysis, there was no discernible difference between the early rehabilitation group and the delayed rehabilitation group at six and twelve months after the surgery in terms of the VAS score, SST score, follow-up rotator cuff healing rate, and the rotator cuff retear rate at the final follow-up. There was no difference in the ASES score between the early and delayed rehabilitation groups six months after the operation. However, although the ASES score in the early rehabilitation group differed significantly from that in the delayed rehabilitation group twelve months after the operation, according to the analysis of the minimal clinically important difference (MCID), the results have no clinical significance.

Conclusions

The improvement in shoulder function following arthroscopic rotator cuff surgery does not differ clinically between early and delayed rehabilitation. When implementing rehabilitation following rotator cuff repair, it is essential to consider the paradoxes surrounding shoulder range of motion and tendon anatomic healing. A program that allows for flexible progression based on the patient's ability to meet predetermined clinical goals or criteria may be a better option.

Open-Top Patterned Hydrogel-Laden 3D Glioma Cell Cultures for Creation of Dynamic Chemotactic Gradients to Direct Cell Migration

The laminar flow profiles in microfluidic systems coupled to rapid diffusion at flow streamlines have been widely utilized to create well-controlled chemical gradients in cell cultures for spatially directing cell migration. However, within hydrogel-based closed microfluidic systems of limited depth (≤0.1 mm), the biomechanical cues for the cell culture are dominated by cell interactions with channel surfaces rather than with the hydrogel microenvironment. Also, leaching of poly(dimethylsiloxane) (PDMS) constituents in closed systems and the adsorption of small molecules to PDMS alter chemotactic profiles. To address these limitations, we present the patterning and integration of a PDMS-free open fluidic system, wherein the cell-laden hydrogel directly adjoins longitudinal channels that are designed to create chemotactic gradients across the 3D culture width, while maintaining uniformity across its ∼1 mm depth to enhance cell-biomaterial interactions. This hydrogel-based open fluidic system is assessed for its ability to direct migration of U87 glioma cells using a hybrid hydrogel that includes hyaluronic acid (HA) to mimic the brain tumor microenvironment and gelatin methacrylate (GelMA) to offer the adhesion motifs for promoting cell migration. Chemotactic gradients to induce cell migration across the hydrogel width are assessed using the chemokine CXCL12, and its inhibition by AMD3100 is validated. This open-top hydrogel-based fluidic system to deliver chemoattractant cues over square-centimeter-scale areas and millimeter-scale depths can potentially serve as a robust screening platform to assess emerging glioma models and chemotherapeutic agents to eradicate them.

The CREB1/WNK1 axis promotes the tumorigenesis of ovarian cancer via regulating HIF-1

The aim of this study was to explore the functions and molecular mechanisms of the WNK lysine deficient protein kinase 1 (WNK1) in the development of ovarian cancer. Firstly, loss- and gain-of-function assays were carried out and subsequently cell proliferation, apoptosis, invasion and migration were detected. Furthermore, WNK1 action on glucose uptake, lactate production and adenosine triphosphate (ATP) level were assessed. The roles of WNK1 on cisplatin resistance were explored using CCK-8, colony formation, and flow cytometry in vitro. Immunohistochemistry, Western blot and qRT-PCR were conducted to determine the protein and mRNA expression. Additionally, tumor growth in vivo was also monitored. We found that the overexpression of WNK1 predicted a bad prognosis of ovarian cancer patients. WNK1 enhanced the malignant behavior and facilitated glycolysis of ovarian cancer cells. Moreover, WNK1 increased cisplatin resistance in ovarian cancer cells. Mechanistically, we found that WNK1 expression was promoted by CREB1 at the transcriptional level. And CREB1 could facilitate ovarian cancer cells malignant behavior through target upregulating WNK1. Besides, we also showed that WNK1 facilitated the malignant behavior by accelerating HIF-1 expression. In xenograft tumor tissues, the downregulation of WNK1 significantly reduced HIF-1α expression. These data demonstrated that the CREB1/WNK1 axis could promote the tumorigenesis of ovarian cancer via accelerating HIF-1 expression, suggesting that the CREB1/WNK1 axis could be a potential target during the therapy of ovarian cancer.

PCAO2: an ontology for integration of prostate cancer associated genotypic, phenotypic and lifestyle data

Disease ontologies facilitate the semantic organization and representation of domain-specific knowledge. In the case of prostate cancer (PCa), large volumes of research results and clinical data have been accumulated and needed to be standardized for sharing and translational researches. A formal representation of PCa-associated knowledge will be essential to the diverse data standardization, data sharing and the future knowledge graph extraction, deep phenotyping and explainable artificial intelligence developing. In this study, we constructed an updated PCa ontology (PCAO2) based on the ontology development life cycle. An online information retrieval system was designed to ensure the usability of the ontology. The PCAO2 with a subclass-based taxonomic hierarchy covers the major biomedical concepts for PCa-associated genotypic, phenotypic and lifestyle data. The current version of the PCAO2 contains 633 concepts organized under three biomedical viewpoints, namely, epidemiology, diagnosis and treatment. These concepts are enriched by the addition of definition, synonym, relationship and reference. For the precision diagnosis and treatment, the PCa-associated genes and lifestyles are integrated in the viewpoint of epidemiological aspects of PCa. PCAO2 provides a standardized and systematized semantic framework for studying large amounts of heterogeneous PCa data and knowledge, which can be further, edited and enriched by the scientific community. The PCAO2 is freely available at https://bioportal.bioontology.org/ontologies/PCAO, http://pcaontology.net/ and http://pcaontology.net/mobile/.

Performance of ChatGPT on Chinese national medical licensing examinations: a five-year examination evaluation study for physicians, pharmacists and nurses

BACKGROUND

Large language models like ChatGPT have revolutionized the field of natural language processing with their capability to comprehend and generate textual content, showing great potential to play a role in medical education. This study aimed to quantitatively evaluate and comprehensively analysis the performance of ChatGPT on three types of national medical examinations in China, including National Medical Licensing Examination (NMLE), National Pharmacist Licensing Examination (NPLE), and National Nurse Licensing Examination (NNLE).

METHODS

We collected questions from Chinese NMLE, NPLE and NNLE from year 2017 to 2021. In NMLE and NPLE, each exam consists of 4 units, while in NNLE, each exam consists of 2 units. The questions with figures, tables or chemical structure were manually identified and excluded by clinician. We applied direct instruction strategy via multiple prompts to force ChatGPT to generate the clear answer with the capability to distinguish between single-choice and multiple-choice questions.

RESULTS

ChatGPT failed to pass the accuracy threshold of 0.6 in any of the three types of examinations over the five years. Specifically, in the NMLE, the highest recorded accuracy was 0.5467, which was attained in both 2018 and 2021. In the NPLE, the highest accuracy was 0.5599 in 2017. In the NNLE, the most impressive result was shown in 2017, with an accuracy of 0.5897, which is also the highest accuracy in our entire evaluation. ChatGPT's performance showed no significant difference in different units, but significant difference in different question types. ChatGPT performed well in a range of subject areas, including clinical epidemiology, human parasitology, and dermatology, as well as in various medical topics such as molecules, health management and prevention, diagnosis and screening.

CONCLUSIONS

These results indicate ChatGPT failed the NMLE, NPLE and NNLE in China, spanning from year 2017 to 2021. but show great potential of large language models in medical education. In the future high-quality medical data will be required to improve the performance.

Extracting laboratory test information from paper-based reports

BACKGROUND

In the healthcare domain today, despite the substantial adoption of electronic health information systems, a significant proportion of medical reports still exist in paper-based formats. As a result, there is a significant demand for the digitization of information from these paper-based reports. However, the digitization of paper-based laboratory reports into a structured data format can be challenging due to their non-standard layouts, which includes various data types such as text, numeric values, reference ranges, and units. Therefore, it is crucial to develop a highly scalable and lightweight technique that can effectively identify and extract information from laboratory test reports and convert them into a structured data format for downstream tasks.

METHODS

We developed an end-to-end Natural Language Processing (NLP)-based pipeline for extracting information from paper-based laboratory test reports. Our pipeline consists of two main modules: an optical character recognition (OCR) module and an information extraction (IE) module. The OCR module is applied to locate and identify text from scanned laboratory test reports using state-of-the-art OCR algorithms. The IE module is then used to extract meaningful information from the OCR results to form digitalized tables of the test reports. The IE module consists of five sub-modules, which are time detection, headline position, line normalization, Named Entity Recognition (NER) with a Conditional Random Fields (CRF)-based method, and step detection for multi-column. Finally, we evaluated the performance of the proposed pipeline on 153 laboratory test reports collected from Peking University First Hospital (PKU1).

RESULTS

In the OCR module, we evaluate the accuracy of text detection and recognition results at three different levels and achieved an averaged accuracy of 0.93. In the IE module, we extracted four laboratory test entities, including test item name, test result, test unit, and reference value range. The overall F1 score is 0.86 on the 153 laboratory test reports collected from PKU1. With a single CPU, the average inference time of each report is only 0.78 s.

CONCLUSION

In this study, we developed a practical lightweight pipeline to digitalize and extract information from paper-based laboratory test reports in diverse types and with different layouts that can be adopted in real clinical environments with the lowest possible computing resources requirements. The high evaluation performance on the real-world hospital dataset validated the feasibility of the proposed pipeline.

A genetic mosaic mouse model illuminates the pre-malignant progression of basal-like breast cancer

Basal-like breast cancer (BLBC) is highly aggressive, and often characterized by BRCA1 and p53 deficiency. Although conventional mouse models enabled the investigation of BLBC at malignant stages, its initiation and pre-malignant progression remain understudied. Here, we leveraged a mouse genetic system known as mosaic analysis with double markers (MADM) to study BLBC initiation by generating rare GFP+Brca1, p53-deficient mammary cells alongside RFP+ wild-type sibling cells. After confirming the close resemblance of mammary tumors arising in this model to human BLBC at both transcriptomic and genomic levels, we focused our studies on the pre-malignant progression of BLBC. Initiated GFP+ mutant cells showed a stepwise pre-malignant progression trajectory from focal expansion to hyper-alveolarization and then to micro-invasion. Furthermore, despite morphological similarities to alveoli, hyper-alveolarized structures actually originate from ductal cells based on twin-spot analysis of GFP-RFP sibling cells. Finally, luminal-to-basal transition occurred exclusively in cells that have progressed to micro-invasive lesions. Our MADM model provides excellent spatiotemporal resolution to illuminate the pre-malignant progression of BLBC, and should enable future studies on early detection and prevention for this cancer.

An artificial intelligence method for predicting postoperative urinary incontinence based on multiple anatomic parameters of MRI

Background

Deep learning methods are increasingly applied in the medical field; however, their lack of interpretability remains a challenge. Captum is a tool that can be used to interpret neural network models by computing feature importance weights. Although Captum is an interpretable model, it is rarely used to study medical problems, and there is a scarcity of data regarding MRI anatomical measurements for patients with prostate cancer after undergoing Robotic-Assisted Radical Prostatectomy (RARP). Consequently, predictive models for continence that use multiple types of anatomical MRI measurements are limited.

Methods

We explored the energy efficiency of deep learning models for predicting continence by analyzing MRI measurements. We analyzed and compared various statistical models and provided reference examples for the clinical application of interpretable deep-learning models. Patients who underwent RARP at our institution between July 2019 and December 2020 were included in this study. A series of clinical MRI anatomical measurements from these patients was used to discover continence features, and their impact on continence was primarily evaluated using a series of statistical methods and computational models.

Results

Age and six other anatomical measurements were identified as the top seven features of continence by the proposed model UINet7 with an accuracy of 0.97, and the first four of these features were also found by primary statistical analysis.

Conclusions

This study fills the gaps in the in-depth investigation of continence features after RARP due to the limitations of clinical data and applicable models. We provide a pioneering example of the application of deep-learning models to clinical problems. The interpretability analysis of deep learning models has the potential for clinical applications.

Large-Scale Biomedical Relation Extraction Across Diverse Relation Types: Model Development and Usability Study on COVID-19

BACKGROUND

Biomedical relation extraction (RE) is of great importance for researchers to conduct systematic biomedical studies. It not only helps knowledge mining, such as knowledge graphs and novel knowledge discovery, but also promotes translational applications, such as clinical diagnosis, decision-making, and precision medicine. However, the relations between biomedical entities are complex and diverse, and comprehensive biomedical RE is not yet well established.

OBJECTIVE

We aimed to investigate and improve large-scale RE with diverse relation types and conduct usability studies with application scenarios to optimize biomedical text mining.

METHODS

Data sets containing 125 relation types with different entity semantic levels were constructed to evaluate the impact of entity semantic information on RE, and performance analysis was conducted on different model architectures and domain models. This study also proposed a continued pretraining strategy and integrated models with scripts into a tool. Furthermore, this study applied RE to the COVID-19 corpus with article topics and application scenarios of clinical interest to assess and demonstrate its biological interpretability and usability.

RESULTS

The performance analysis revealed that RE achieves the best performance when the detailed semantic type is provided. For a single model, PubMedBERT with continued pretraining performed the best, with an F1-score of 0.8998. Usability studies on COVID-19 demonstrated the interpretability and usability of RE, and a relation graph database was constructed, which was used to reveal existing and novel drug paths with edge explanations. The models (including pretrained and fine-tuned models), integrated tool (Docker), and generated data (including the COVID-19 relation graph database and drug paths) have been made publicly available to the biomedical text mining community and clinical researchers.

CONCLUSIONS

This study provided a comprehensive analysis of RE with diverse relation types. Optimized RE models and tools for diverse relation types were developed, which can be widely used in biomedical text mining. Our usability studies provided a proof-of-concept demonstration of how large-scale RE can be leveraged to facilitate novel research.

Association Between Urinary Bisphenols and Body Composition Among American Adults: Cross-Sectional National Health and Nutrition Examination Survey Study

BACKGROUND

Bisphenol A (BPA), bisphenol S (BPS), and bisphenol F (BPF) are widely used in various consumer products. They are environmental contaminants with estrogenic properties that have been linked to various health outcomes. Understanding their impact on body composition is crucial for identifying potential health risks and developing preventive strategies. However, most current studies have only focused on their relationship with BMI.

OBJECTIVE

This study aimed to investigate the association between urinary levels of BPA, BPS, and BPF and body composition, including BMI, lean mass, and fat mass, in a large population-based sample.

METHODS

We conducted a cross-sectional analysis using data from the National Health and Nutrition Examination Survey 2003-2016. Body composition data were assessed using dual-energy X-ray absorptiometry, which provided precise measurements of lean mass, fat mass, and other indicators. We used multivariate linear regression models to estimate the associations, adjusting for potential confounders such as age, gender, race, socioeconomic factors, and lifestyle variables.

RESULTS

The results revealed significant associations between bisphenol exposure and body composition. After adjusting for covariates, BPS showed a positive association with BMI, with quartiles 3 and 4 having 0.91 (95% CI 0.34-1.48) and 1.15 (95% CI 0.55-1.74) higher BMI, respectively, compared with quartile 1 (P<.001). BPA was negatively associated with total lean mass (TLM) and appendicular lean mass, with quartiles 2, 3, and 4 having -7.85 (95% CI -11.44 to -4.25), -12.33 (95% CI -16.12 to -8.54), and -11.08 (95% CI -15.16 to -7.01) lower TLM, respectively, compared with quartile 1 (P<.001). BPS was negatively associated with TLM, with quartiles 3 (β=-10.53, 95% CI -16.98 to -4.08) and 4 (β=-11.14, 95% CI -17.83 to -4.45) having significantly lower TLM (P=.005). Both BPA and BPS showed a positive dose-response relationship with trunk fat (BPA: P=.002; BPS: P<.001) and total fat (BPA: P<.001; BPS: P=.01). No significant association was found between BPF and any body composition parameter.

CONCLUSIONS

This large-sample study highlights the associations between urinary levels of BPA and BPS and alterations in body composition, including changes in lean mass, fat mass, and regional fat distribution. These findings underscore the importance of understanding the potential health risks associated with bisphenol exposure and emphasize the need for targeted interventions to mitigate adverse effects on body composition.

Impact of apolipoprotein A1 on tumor immune microenvironment, clinical prognosis and genomic landscape in hepatocellular carcinoma

Background

Current knowledge on apolipoprotein A1 (APOA1) in hepatocellular carcinoma (HCC) is fragmented and even contradictory. Multi-dimensional analyses are required to comprehensively elucidate its value and underlying mechanism.

Methods

We collected 49 RNA-seq datasets, 40 cell line types data and 70 scRNA pan-cancer datasets public available, including 17 HCC datasets (1754 tumor samples), and enrolled 73 pairs of HCC tissue and 516 blood samples independently from our clinics. APOA1 impacting on the HCC tumor microenvironment (TME) was analyzed using intensive data mining. Methylation sequencing, flow cytometry, quantitative PCR, western blot, immunohistochemistry and clinical chemistry assays were conducted for wet experimental investigation.

Results

The APOA1 ontology fingerprint indicated that it played various crucial biological roles in HCC, primarily involved in cholesterol efflux. Consistent findings at histology, serology, and clinical follow-up revealed that high APOA1 was a good prognosis indicator of HCC. Hypermethylation in the APOA1 promoter region was found in clinical samples which is in accordance with the reduction of APOA1 in HCC. The cell cycle, DNA replication, mismatch repair pathways, and tumor cell proliferation were less observed in the HCC APOA1high subgroup. The favorable immunoregulatory abilities of APOA1 showed interesting findings: a positive correlation between APOA1 and anti-tumor immune cells (NK, CD8+ T cells) and a negative association with immune cells exerting immunosuppressive effects, including M2 macrophages.

Conclusion

This is an integrative multidimensional exploration of APOA1 using bioinformatics and experiments. Both the prognostic value and anti-tumor effects based on APOA1 panoramic exploration in the HCC TME demonstrate a new potential clinical target for HCC assessment and intervention in the future.

Dichotomous ovarian cancer-initiating potential of Pax8+ cells revealed by a mouse genetic mosaic model

Different cellular compartments within a tissue present distinct cancer-initiating capacities. Current approaches to dissect such heterogeneity require cell-type-specific genetic tools based on a well-understood lineage hierarchy, which are lacking for many tissues. Here, we circumvented this hurdle and revealed the dichotomous capacity of fallopian tube Pax8+ cells in initiating ovarian cancer, utilizing a mouse genetic system that stochastically generates rare GFP-labeled mutant cells. Through clonal analysis and spatial profiling, we determined that only clones founded by rare, stem/progenitor-like Pax8+ cells can expand on acquiring oncogenic mutations whereas vast majority of clones stall immediately. Furthermore, expanded mutant clones undergo further attrition: many turn quiescent shortly after the initial expansion, whereas others sustain proliferation and manifest a bias toward Pax8+ fate, underlying early pathogenesis. Our study showcases the power of genetic mosaic system-based clonal analysis for revealing cellular heterogeneity of cancer-initiating capacity in tissues with limited prior knowledge of lineage hierarchy.

A genetic mosaic mouse model illuminates the pre-malignant progression of basal-like breast cancer

Basal-like breast cancer is an aggressive breast cancer subtype, often characterized by a deficiency in BRCA1 function and concomitant loss of p53 . While conventional mouse models enable the investigation of its malignant stages, one that reveals its initiation and pre-malignant progression is lacking. Here, we leveraged a mouse genetic system known as M osaic A nalysis with D ouble M arkers (MADM) to generate rare GFP-labeled Brca1 , p53 -deficient cells alongside RFP+ wildtype sibling cells in the mammary gland. The mosaicism resembles the sporadic initiation of human cancer and enables spatially resolved analysis of mutant cells in comparison to paired wildtype sibling cells. Mammary tumors arising in the model show transcriptomic and genomic characteristics similar to human basal-like breast cancer. Analysis of GFP+ mutant cells at interval time points before malignancy revealed a stepwise progression of lesions from focal expansion to hyper-alveolarization and then to micro-invasion. These stereotyped morphologies indicate the pre-malignant stage irrespective of the time point at which it is observed. Paired analysis of GFP-RFP siblings during focal expansion suggested that hyper-alveolarized structures originate from ductal rather than alveolar cells, despite their morphological similarities to alveoli. Evidence for luminal-to-basal transition at the pre-malignant stages was restricted to cells that had escaped hyper-alveoli and progressed to micro-invasive lesions. Our MADM-based mouse model presents a useful tool for studying the pre-malignancy of basal-like breast cancer.

Summary statement

A mouse model recapitulates the process of human basal-like breast tumorigenesis initiated from sporadic Brca1, p53 -deficient cells, empowering spatially-resolved analysis of mutant cells during pre-malignant progression.

Ta4C3-Modulated MOF-Derived 3D Crosslinking Network of VO2(B)@Ta4C3 for High-Performance Aqueous Zinc Ion Batteries

A two-dimensional MXene (Ta₄C₃) was innovatively used herein to modulate the space group and electronic properties of vanadium oxides, and the MXene/metal-organic framework (MOF) derivative VO₂(B)@Ta₄C₃ with 3D network cross-linking was prepared, which was then employed as a cathode to improve the performance of aqueous zinc ion batteries (ZIBs). A novel method combining HCl/LiF and hydrothermal treatments was used to etch Ta₄AlC₃ to obtain a large amount of accordion-like Ta₄C₃, and the V-MOF was then hydrothermally grown on the surface of the stripped Ta₄C₃ MXene. During the annealing process of V-MOF@Ta₄C₃, the addition of Ta₄C₃ MXene liberates the V-MOF from agglomerative stacking, allowing it to show additional active sites. More significantly, Ta₄C₃ prevents the V-MOF in the composite structure from converting into V₂O₅ of space group Pmmn but into VO₂(B) of space group C2/m after annealing. A considerable advantage of VO₂(B) for Zn²⁺ intercalation is provided by the negligible structural transformation during the intercalation process and the special tunnel transport channels, which have an enormous area (0.82 nm² along the b axis). According to first-principles calculations, there is a strong interfacial interaction between VO₂(B) and Ta₄C₃, which deliver remarkable electrochemical activity and kinetic performances for the storage of Zn²⁺. Therefore, the ZIBs prepared with the VO₂(B)@Ta₄C₃ cathode material exhibit an ultra-high capacity of 437 mA h·g^-1 at 0.1 A·g^-1 while showing good cycle performance and dynamic performance. This study will offer a fresh approach and a reference for creating metal oxide/MXene composite structures.

ViMRT: a text-mining tool and search engine for automated virus mutation recognition

MOTIVATION

Virus mutation is one of the most important research issues which plays a critical role in disease progression and has prompted substantial scientific publications. Mutation extraction from published literature has become an increasingly important task, benefiting many downstream applications such as vaccine design and drug usage. However, most existing approaches have low performances in extracting virus mutation due to both lack of precise virus mutation information and their development based on human gene mutations.

RESULTS

We developed ViMRT, a text-mining tool and search engine for automated virus mutation recognition using natural language processing. ViMRT mainly developed 8 optimized rules and 12 regular expressions based on a development dataset comprising 830 papers of 5 human severe disease-related viruses. It achieved higher performance than other tools in a test dataset (1662 papers, 99.17% in F1-score) and has been applied well to two other viruses, influenza virus and severe acute respiratory syndrome coronavirus-2 (212 papers, 96.99% in F1-score). These results indicate that ViMRT is a high-performance method for the extraction of virus mutation from the biomedical literature. Besides, we present a search engine for researchers to quickly find and accurately search virus mutation-related information including virus genes and related diseases.

AVAILABILITY AND IMPLEMENTATION

ViMRT software is freely available at http://bmtongji.cn:1225/mutation/index.

Potential of Polyethyleneimine as an Adjuvant To Prepare Long-Term and Potent Antifungal Nanovaccine

Background

Candida albicans infections are particularly prevalent in immunocompromised patients. Even with appropriate treatment with current antifungal drugs, the mortality rate of invasive candidiasis remains high. Many positive results have been achieved in the current vaccine development. There are also issues such as the vaccine's protective effect is not persistent. Considering the functionality and cost of the vaccine, it is important to develop safe and efficient new vaccines with long-term effects. In this paper, an antifungal nanovaccine with Polyethyleneimine (PEI) as adjuvant was constructed, which could elicit more effective and long-term immunity via stimulating B cells to differentiate into long-lived plasma cells.

Materials and Methods

Hsp90-CTD is an important target for protective antibodies during disseminated candidiasis. Hsp90-CTD was used as the antigen, then introduced SDS to "charge" the protein and added PEI to form the nanovaccine. Dynamic light scattering and transmission electron microscope were conducted to identify the size distribution, zeta potential, and morphology of nanovaccine. The antibody titers in mice immunized with the nanovaccine were measured by ELISA. The activation and maturation of long-lived plasma cells in bone marrow by nanovaccine were also investigated via flow cytometry. Finally, the kidney of mice infected with Candida albicans was stained with H&E and PAS to evaluate the protective effect of antibody in serum produced by immunized mice.

Results

Nanoparticles (NP) formed by Hsp90-CTD and PEI are small, uniform, and stable. NP had an average size of 116.2 nm with a PDI of 0.13. After immunizing mice with the nanovaccine, it was found that the nano-group produced antibodies faster and for a longer time. After 12 months of immunization, mice still had high and low levels of antibodies in their bodies. Results showed that the nanovaccine could promote the differentiation of B cells into long-lived plasma cells and maintain the long-term existence of antibodies in vivo. After immunization, the antibodies in mice could protect the mice infected by C. albicans.

Conclusion

As an adjuvant, PEI can promote the differentiation of B cells into long-lived plasma cells to maintain long-term antibodies in vivo. This strategy can be adapted for the future design of vaccines.

Oxygen-Terminated Nb2CO2 MXene with Interfacial Self-Assembled COF as a Bifunctional Catalyst for Durable Zinc-Air Batteries

The desirable air cathode in Zn-air batteries (ZABs) that can effectively balance oxygen evolution and oxygen reduction reactions not only needs to adjust the electronic structure of the catalyst but also needs a unique physical structure to cope with the complex gas-liquid environment. In this work, first-principles calculations were carried out to prove that oxygen-terminated Nb₂CO₂ MXene played an active role in enhancing the sluggish reaction of oxygen intermediates. Nb₂CO₂ MXene could also stimulate the spatial accumulation of discharge products, which was beneficial to improve the stability of secondary ZABs. Molecular dynamics simulation was used to show that the confinement effect of COF could effectively regulate the concentration of O₂ on the surface of Nb₂CO₂@COF, which was conducive to an efficient and durable reaction. COF-LZU1 was self-assembled on the interface of Nb₂CO₂ MXene (Nb₂CO₂@COF) for the first time. The Nb₂CO₂@COF electrode had excellent OER/ORR overpotentials with the potential difference (ΔE) of 0.79 V. When applied to the configuration of ZABs, Nb₂CO₂@COF showed a power density of 75 mW cm^-2 and favorable long-term charge/discharge stability, so it could be used as a potential candidate cathode for noble-metal-based catalysts. This idea of combining MXenes and COFs sheds some light on the design of ZABs.

Oxidative stress is involved in the activation of NF-κB signal pathway and immune inflammatory response in grass carp gill induced by cypermethrin and/or sulfamethoxazole

At present, the concentration of environmental pollutants, such as pesticides and antibiotics exposed in environment, especially in aquatic environment is increasing. Research on environmental pollutants has exploded in the last few years. However, studies on the combined effects of pesticides and antibiotics on fish are rare, especially the toxic damage to gill tissue is vague. In this paper, cypermethrin (CMN) and sulfamethoxazole (SMZ) were analyzed and found that there was a strong correlation between the pathways affected by the first 30 genes regulated by CMN and SMZ, respectively. Therefore, the toxic effects of CMN (0.651 μg L^-1) and/or SMZ (0.3 μg L^-1) on grass carp gill were studied in this paper. Histopathology, quantitative real-time PCR, and other methods were used to detect the tissue morphology, oxidative stress level, inflammation, and apoptosis-related indicators of the fish gills after exposure of 42 days. It was found that compared with the single exposure (CMN/SMZ) group, the combined exposure (MIX) group had a more pronounced oxidative stress index imbalance. At the same time, nuclear factor-κB (NF-κB) signal pathway was activated and immuno-inflammatory reaction appeared in MIX group. The expression of tumor necrosis factor (TNF-α) in the rising range is 2.94 times that of the C group, while the expression of interleukin 8 (IL-8) is as high as 32.67 times. This study reveals the harm of CMN and SMZ to fish, and provides a reference and basis for the rational use of pesticides and antibiotics.