From prediction to prevention:Machine learning revolutionizes hepatocellular carcinoma recurrence monitoring

In this editorial,we comment on the article by Zhang et al entitled Development of a machine learning-based model for predicting the risk of early postoperative recurrence of hepatocellular carcinoma.Hepatocellular carcinoma(HCC),which is characterized by high incidence and mortality rates,remains a major global health challenge primarily due to the critical issue of postoperative recurrence.Early recurrence,defined as recurrence that occurs within 2 years posttreatment,is linked to the hidden spread of the primary tumor and significantly impacts patient survival.Traditional predictive factors,including both patient-and treatment-related factors,have limited predictive ability with respect to HCC recurrence.The integration of machine learning algorithms is fueled by the exponential growth of computational power and has revolutionized HCC research.The study by Zhang et al demonstrated the use of a groundbreaking preoperative prediction model for early postoperative HCC recurrence.Challenges persist,including sample size constraints,issues with handling data,and the need for further validation and interpretability.This study emphasizes the need for collaborative efforts,multicenter studies and comparative analyses to validate and refine the model.Overcoming these challenges and exploring innovative approaches,such as multi-omics integration,will enhance personalized oncology care.This study marks a significant stride toward precise,efficient,and personalized oncology practices,thus offering hope for improved patient outcomes in the field of HCC treatment.

Advancing critical care recovery:The pivotal role of machine learning in early detection of intensive care unit-acquired weakness

This editorial explores the significant challenge of intensive care unit-acquiredweakness(ICU-AW),a prevalent condition affecting critically ill patients,characterizedby profound muscle weakness and complicating patient recovery.Highlightingthe paradox of modern medical advances,it emphasizes the urgent needfor early identification and intervention to mitigate ICU-AW's impact.Innovatively,the study by Wang et al is showcased for employing a multilayer perceptronneural network model,achieving high accuracy in predicting ICU-AWrisk.This advancement underscores the potential of neural network models inenhancing patient care but also calls for continued research to address limitationsand improve model applicability.The editorial advocates for the developmentand validation of sophisticated predictive tools,aiming for personalized carestrategies to reduce ICU-AW incidence and severity,ultimately improving patientoutcomes in critical care settings.

Non-coding RNAs:the new central dogma of cancer biology

The central dogma of molecular biology states that the functions of RNA revolve around protein translation.Until the last decade,most researches were geared towards characterization of RNAs as intermediaries in protein translation,namely,messenger RNAs(mRNAs)as temporary copies of genetic information,ribosomal RNAs(rRNAs)as a main component of ribosome,or translators of codon sequence(t RNAs).The statistical reality,however,is that these processes account for less than 2%of the genome,and insufficiently explain the functionality of 98%of transcribed RNAs.Recent discoveries have unveiled thousands of unique non-coding RNAs(ncRNAs)and shifted the perception of them from being"junk"transcriptional products to"yet to be elucidated"—and potentially monumentally important—RNAs.Most ncRNAs are now known as key regulators in various networks in which they could lead to specific cellular responses and fates.In major cancers,ncRNAs have been identified as both oncogenic drivers and tumor suppressors,indicating a complex regulatory network among these ncRNAs.Herein,we provide a comprehensive review of the various ncRNAs and their functional roles in cancer,and the pre-clinical and clinical development of nc RNA-based therapeutics.A deeper understanding of ncRNAs could facilitate better design of personalized therapeutics.