Nanotechnology is revolutionizing the food industry by improving safety, quality, and sustainability through the use of nanosensors and nanomaterials. Several nanosensors are employed, including biosensors for rapid pathogen detection, nanocomposite indicators for food freshness, and quantum dot-based sensors for heavy metal and pesticide detection. Other types include cantilever-based sensors, carbon nanotube-based electrochemical sensors, nanowires, nano-electromechanical systems (NEMS), and luminous nanoparticle labels for targeted detection. These nanosensors outperform standard processes in terms of sensitivity, speed, and selectivity, allowing for real-time food quality monitoring, contamination detection, and spoiling indication. They also provide temperature monitoring, microbiological detection, and color change indicators in smart packaging. Some of the key benefits include improved quality through freshness monitoring, reduced food waste through accurate shelf-life indications, and increased food safety through early pathogen and toxin detection, and support for sustainability through improved supply chain management and safer packaging solutions. However, there are significant barriers to the application of nanotechnology in food and its integration into existing food industry operations.
Alzheimer's disease (AD) is a clinical-biological entity in which pathophysiological changes precede symptoms by years. Biomarkers are essential for early and accurate diagnosis, particularly in the era of disease-modifying therapies requiring biological confirmation. Cerebrospinal fluid (CSF) and amyloid positron emission tomography (amyloid-PET) are the reference standards, but increasing attention is devoted to blood-based biomarkers (BBMs) due to their scalability and cost-effectiveness. In this critical perspective, the authors critically appraise the current evidence supporting plasma phosphorylated tau at threonine 217 (p-tau217) as the leading BBM for AD. They also discuss its analytical performance, biological rationale, and diagnostic accuracy across the AD continuum, its relationship with established CSF, PET, and neuropathological biomarkers, its potential role in identifying patients eligible for disease-modifying therapies, and the main clinical and biological factors influencing its interpretation. Finally, they highlight current limitations, unresolved challenges, and give their future perspectives for the integration of plasma biomarkers into routine clinical practice. BBMs are expected to reshape AD diagnostics. A stepwise approach, using plasma biomarkers as first-line tests followed by confirmatory CSF or PET, is currently the most feasible strategy. Ultimately, highly specific, brain-derived tau biomarkers may enable BBMs to replace CSF biomarkers.
Droplet microfluidics has revolutionized diagnostics, chemical synthesis, and biotechnology. While droplet merging and mixing are routine operations, the opposite processes─coupled enrichment and pinch-off of reagents, analytes, or products─is nontrivial. If accessed, these operations are highly valuable, enabling improved reaction kinetics, increased assay sensitivity, and purification. In this work, we present coupled enrichment and on-demand emission of picoliter-scale daughter droplets from nanoliter-scale parents. The reported approach leverages electrokinetically driven viscosity gradients within droplets to modulate droplet dynamics. Specifically, localized enrichment of a charged viscosity modifier (the polyelectrolyte, alginate) and a charged target species (here, an anionic fluorescent tracer) is accomplished by in-droplet ion concentration polarization (ICP). The results presented here demonstrate that emission is favored by a steep viscosity gradient and that the volume of daughter droplets is programmable. These findings potentiate unprecedented control over droplet-mediated chemistry and bioassays via a tunable process reminiscent of vesicle packaging and pinch-off in biological cells.
Pulsed field ablation (PFA) has revolutionized atrial fibrillation (AF) treatment by leveraging non-thermal irreversible electroporation and relative myocardial tissue selectivity. While early clinical data and large registries demonstrated near-elimination of conventional thermal injuries-including atrioesophageal fistula, persistent phrenic nerve injury, and pulmonary vein stenosis-the ongoing accumulation of real-world experience has revealed a spectrum of energy-specific adverse events (AEs), some of which are relatively unique to PFA, such as coronary artery spasm and hemolysis. Multicenter registries such as MANIFEST-17 K and MANIFEST-US confirm a low overall major AE rate (<1%), highlighting the safety of PFA. However, some of these distinct AEs continue to be reported at clinically significant frequencies. Furthermore, recent reports have raised concerns regarding rare but life-threatening delayed arrhythmic events possibly related to coronary spasm. These findings suggest that the safety profile of PFA for AF has changed to involve different events rather than an overall improvement. Traditional AEs associated with venous access and left atrial instrumentation remain. This review summarizes the evolving landscape of AEs seen with PFA for AF, focusing on energy-specific AEs, their underlying mechanisms, and the clinical implications for procedural optimization and postprocedural management.
Immune checkpoint inhibitors (ICIs) have revolutionized cancer therapy, and their side effects, such as cardiotoxicity, have become critical complications. Herein, we explored the potential mechanism of ICIs-related cardiotoxicity. Healthy male C57BL/6 J mice were intraperitoneally injected with a PD-1/PD-L1 inhibitor (BMS-1) at a total dose of 60 mg/kg. BMS-1 treatment led to cardiac injury, with elevated cardiac enzyme levels, numbers of apoptotic cells, cardiomyocyte cross-sectional areas, and α-SMA expression. BMS-1-induced H9c2 cardiomyocyte injury was immune cell dependent. Compared with the control treatment, BMS-1 treatment did not significantly alter the T-cell composition in the peripheral blood or spleen. However, fluorescence imaging revealed increased numbers of CD3+ T cells, F4/80+ macrophages, and Ly6G+ neutrophils in the hearts of BMS-1-treated mice. In addition, BMS-1 treatment also increased PD-L1 expression and activated inflammatory pathways, including AKT, p38 MAPK, mTOR, and STAT3. Interestingly, the expression of the inflammatory genes Il1b, Il17a, and Ifng; the T-cell activation genes Nkg7 and Cst7; the exhaustion genes Klrg1 and Tigit; and the transcription factors Tbx21 and Rora were markedly elevated in the thymus, but their expression was unchanged in the peripheral blood and spleen. Furthermore, electron microscopy revealed mitochondrial swelling and lipid droplets in the hearts of BMS-1-treated mice. Compared with those in control hearts, the levels of the lipid metabolism proteins p-ACC, p-ACLY, FASN, and Lipin 1 were consistently upregulated in the hearts of BMS-1-treated mice. Taken together, these findings suggested that the PD-1/PD-L1 inhibitor BMS-1 induced inflammation in the heart. Alterations in cardiac lipid metabolism might be correlated with ICIs-related cardiotoxicity.
The recent discovery of three-dimensional (3D) quantum Hall effect (QHE) of Fermi arcs in topological semimetals has revolutionized our understanding of Hall physics in 3D systems. However, its most prominent hallmark, the one-sided chiral hinge states of Fermi arcs, has thus far never been experimentally observed in any physical system. Here, we report the first photonic realization of 3D QHE of Fermi arcs and directly observe the one-sided chiral hinge states of Fermi arcs in an inhomogeneous magnetic Weyl photonic crystal under a pseudomagnetic field (PMF) with time-reversal symmetry breaking. We experimentally demonstrate that the PMF quantizes both the bulk and Fermi arc surface states into Landau plateaus, giving rise to chiral Landau levels and robust one-sided chiral hinge states localized at only one edge on the front surface and at the opposite edge on the back surface, both of which are the signatures of 3D QHE of Fermi arcs. Moreover, we show that the one-sided chiral hinge states of Fermi arcs can be switched between the two pairs of diagonal hinges by reversing the PMF. Our work not only provides an ideal platform for exploring 3D quantum Hall physics but also opens new avenues for the design of robust photonic devices.
Chimeric antigen receptor (CAR) T-cell therapy has revolutionized the treatment landscape for relapsed/refractory (R/R) lymphoma. However, heterogeneous responses and treatment-related toxicities remain significant challenges. The prognostic nutritional index (PNI), reflecting both nutritional status and systemic immune competence, has emerged as a potential biomarker in various malignancies. This study aimed to evaluate the predictive value of the PNI assessed specifically prior to lymphodepletion in patients with R/R lymphoma receiving CAR T-cell therapy. We retrospectively analyzed 449 patients with R/R lymphoma treated with CAR T cells. The PNI was calculated using serum albumin levels and absolute lymphocyte counts measured before administering lymphodepleting chemotherapy. The optimal PNI cutoff for predicting survival was determined to be 39.2 using maximally selected rank statistics. The patients were stratified into high-PNI (> 39.2, n = 363) and low-PNI (≤ 39.2, n = 86) groups on the basis of the PNI cutoff value. The median age of the patients was 52 years. All patients had R/R aggressive B-cell lymphoma and were treated with CAR T cells. Compared with patients in the low-PNI group, patients in the high-PNI group achieved significantly superior clinical responses, with higher overall response rates (ORRs: 65.5% vs. 44.2%, P < 0.001) and complete response rates (CRRs: 52.2% vs. 27.9%, P < 0.001). The median follow-up period was 33.12 months, and long-term survival markedly improved among patients in the high-PNI group; the 5-year overall survival (OS) rates were 59.02% vs. 21.88% (P < 0.001), and the 5-year progression-free survival (PFS) rates were 42.69% vs. 13.29% (P < 0.001) for patients in the high- and low-PNI groups, respectively. In terms of safety, multivariate analysis confirmed that a high PNI independently reduced the risk of any-grade CRS (P = 0.047), but was not significantly associated with grade ≥ 3 CRS (P = 0.121). No significant association was observed between a high PNI and the occurrence or severity of ICANS (all P > 0.05). Multivariate analysis revealed that a PNI > 39.2 remained an independent predictor of both OS (HR = 0.425, P < 0.001) and PFS (HR = 0.542, P < 0.001). The pre-lymphodepletion PNI is a simple, noninvasive, and robust tool for predicting therapeutic efficacy, long-term survival, and treatment-related toxicity in patients with R/R lymphoma receiving CAR T-cell therapy. A PNI threshold of 39.2 provides a valuable reference for risk stratification and clinical management.
Machine learning has revolutionized numerous industrial domains. Despite recent advances, machine learning models remain vulnerable to adversarial threats. Adversarial machine learning is a field that studies these vulnerabilities to build robust machine learning models. Quantum machine learning is an interdisciplinary field that bridges quantum computing and classical machine learning. While quantum machine learning shows potentials to outperform classical machine learning in complex tasks such as regression, classification, and generative modeling, it remains vulnerable to adversarial attacks. Given the recent advancements in quantum computing and machine learning, the quantum adversarial machine learning field has emerged to study the vulnerabilities of quantum machine learning, possible attacks, and novel quantum-enhanced defense strategies. In this survey, we provide a detailed overview on quantum adversarial machine learning and explore the existing attacks and countermeasures. We also review the theoretical underpinnings of this area, emerging trends, and critical challenges.
The advent of third-generation sequencing, particularly Oxford Nanopore Technologies (ONT), has revolutionized epigenetic studies by enabling direct detection of DNA methylation modifications and single-base resolution profiling of methylation patterns. While this technology has been predominantly utilized in human and bacterial research, its applications in livestock and poultry remain limited. In this study, we employed ONT sequencing to construct comprehensive 5-methylcytosine modification maps for ten representative pig breeds, explored the mechanism of high altitude adaptive methylation and allele-specific methylation events in these pigs. Through genome-wide integration of sequencing data, we identified 27,857,021 CpG sites, with 71.5% (19,836,456) shared across pigs. Comparative differential methylation analysis between high-altitude and low-altitude pigs revealed four candidate genes (CALM1, HBB, PRKCQ and RAMP1) and the Sp1 transcription factor as potential key regulators of hypoxic adaptation. Notably, Tibetan pigs exhibited promoter hypomethylation patterns at the CALM1 locus, correlating with its consistently elevated expression confirmed by public transcriptomic databases. Allele-specific methylation (ASM) analysis integrated with transcriptomic profiles demonstrated significant enrichment of ASM events in promoters or exons of allele-specific expression (ASE) genes, suggesting synergistic regulatory mechanisms between epigenetic modifications and allelic expression patterns. Our results provided a high-resolution DNA methylation atlas based on long-read sequencing encompassing ten representative pigs across Eurasia, and identified hypoxic adaption-related genes (CALM1, etc.) and a transcription factor (Sp1) based on the unique physiological characteristics of Tibetan pigs. Further, combined with transcriptome data, it was demonstrated ASM and ASE events are synergistic and expressions of ASE genes may be regulated by ASM. This study offers valuable insights into the epigenetic mechanisms underlying adaptation and gene regulation in pigs.
Advances in molecular technologies have revolutionized veterinary parasitology, providing highly sensitive and specific tools for the detection, characterization, and surveillance of parasites in domestic and wildlife species. Approaches such as next-generation sequencing, metabarcoding, and metagenomics have significantly enhanced the ability to identify previously unknown or uncultivable species, detect complex coinfections, and deepen our understanding of parasite genetic diversity, evolution, and population dynamics. Beyond their impact on laboratory diagnostics, these tools have proven essential for the early detection of zoonoses, environmental monitoring, and the development of integrated surveillance systems under the One Health framework. This review synthesizes the major technological advances and their practical applications in both global and Latin American contexts, particularly Brazilian, highlighting how the incorporation of these tools has the potential to transform strategies for surveillance, prevention, and response to emerging and re-emerging parasitic diseases. Challenges related to standardization, cost, infrastructure, and technology transfer are also discussed, along with future perspectives for large-scale implementation aimed at strengthening diagnostic capacity and epidemiological surveillance in the face of increasing parasitic threats in a rapidly changing world. Os avanços nas tecnologias moleculares têm revolucionado a parasitologia veterinária, proporcionando ferramentas altamente sensíveis e específicas para a detecção, caracterização e vigilância de parasitos em espécies domésticas e silvestres. Abordagens como o sequenciamento de nova geração, o metabarcoding e a metagenômica ampliaram significativamente a capacidade de identificar espécies anteriormente desconhecidas ou não cultiváveis, detectar coinfecções complexas e aprofundar a compreensão sobre a diversidade genética, evolução e dinâmica populacional dos parasitos. Além do impacto no diagnóstico laboratorial, essas ferramentas têm se mostrado essenciais para a detecção precoce de zoonoses, o monitoramento ambiental e o desenvolvimento de sistemas de vigilância integrados no contexto do conceito One Health. Esta revisão sintetiza os principais avanços tecnológicos e suas aplicações práticas, tanto no cenário global quanto no contexto latino-americano, em especial o brasileiro, destacando como a incorporação dessas ferramentas tem potencial para transformar estratégias de vigilância, prevenção e resposta frente a doenças parasitárias emergentes e reemergentes. Também são discutidos os desafios relacionados à padronização, custos, infraestrutura e transferência de tecnologia, além das perspectivas futuras para sua implementação em larga escala, com o objetivo de fortalecer a capacidade diagnóstica e a vigilância epidemiológica diante das crescentes ameaças parasitárias em um mundo em rápida transformação.
Acute leukemia remains a life-threatening hematologic malignancy with historically poor outcomes in relapsed/refractory and elderly patients. Over the past decade, measurable residual disease (MRD) has evolved from a prognostic indicator to a core determinant of risk stratification and clinical decision-making, driving a paradigm shift toward precision medicine. Technological innovations-including leukemia stem cell (LSC)-directed MRD detection, single-cell sequencing, and personalized digital polymerase chain reaction (PCR)-have markedly improved the sensitivity and specificity of MRD monitoring, enabling the early identification of patients at ultrahigh risk of relapse. Concurrently, targeted therapy has moved from salvage to frontline standard care; the use of FLT3, IDH1/2, and BCR-ABL1 inhibitors combined with chemotherapy or immunotherapy has significantly prolonged remission, improved MRD negativity rates, and redefined prognostic stratification. Novel cellular therapies, particularly CD19/CD22-targeted chimeric antigen receptor T (CAR-T) and bispecific T-cell engagers, have revolutionized the treatment of relapsed/refractory B-cell acute lymphoblastic leukemia, and allogeneic hematopoietic stem cell transplantation (allo-HSCT), optimized by the "Beijing Protocol" for haploidentical donors, remains the cornerstone of curative intent. Low-toxicity regimens, such as venetoclax plus hypomethylating agents, have transformed care for elderly or unfit patients, shifting goals from palliation to long-term survival. Despite these advances, challenges, including antigen escape, CAR-T-cell persistence, graft-versus-host disease, and treatment accessibility, persist. This commentary summarizes landmark progress in MRD-guided precision stratification, targeted therapy, cellular immunotherapy, and allo-HSCT; discusses unresolved clinical bottlenecks; and proposes future directions centered on dynamic MRD monitoring, personalized targeted-immunotherapy combinations, and risk-adapted transplantation strategies to further improve cure rates and long-term survival across all acute leukemia subtypes.
MALDI-TOF mass spectrometry has revolutionized microbial identification in the clinical microbiology laboratory. However, barriers in the analysis of mass spectra with databases different from the proprietary prevent this technology from achieving its full potential. The goal of this study was to develop an informatics tool capable of converting peak lists obtained from MALDI-TOF mass spectra to an XML compatible with open data bases, such as MicrobeNet. This innovation makes it possible to overcome limitations in interoperability between commercial platforms, allowing for the optimization and decentralization of microbial identification. A set of mass spectra acquired with MALDI Biotyper and VITEK MS were analyzed, showing high concordance between the converter approach and the standard workflow (MALDI Biotyper: 96.4% (95% CI: 89.4-99.4%) VITEK MS: 87.4% (95% CI: 83.0-91.1%)). This converter promises to become a cost-effective alternative to broaden the clinical microbiology laboratory's diagnostic capacity.
For decades, software for molecular visualization has been a cornerstone of research and education in the chemical and structural sciences. It is unfortunate, however, that consumer devices enable only flat 2D inputs and outputs, which brings two problems. First, traditional 2D screen-mouse interfaces struggle to convey the inherently three-dimensional nature of molecules and their interactions. Second, probably worse, the thoughtful manipulation of molecular structures in three dimensions is very hard to achieve with 2D peripherals. Immersive technologies under the umbrella of eXtended Reality (XR, including augmented and virtual reality, AR and VR) promise to revolutionize how we learn, teach, and conduct research in the chemical and structural sciences, by assisting both how we see and how we manipulate molecules in 3D. This chapter chronicles the development of the "MolecularWeb" ecosystem, a suite of web-based tools designed to make immersive molecular visualization and interactive modeling and simulations widely accessible to a broad audience. Centralized at https://molecularweb.org , these tools have found wide applicability in education and science communication, promising to directly assist research as well. We describe first MolecularWeb, which delivers activities and tools for education in pass-through and mirror-like AR using regular devices like phones, tablets, and computers. We then cover MolecularWebXR, a platform for multiuser immersion using WebXR-enabled headsets-yet remaining accessible with simpler devices-that brings content about chemistry, materials sciences, and structural biology in formats engaging for educational and science outreach activities, also serving for immersive scientific discussions. A short visit to PDB2AR will show how users can create tailored content for MolecularWebXR and also stand-alone AR and VR experiences. We finally delve into our HandMol prototypes, which allow for immersive visualization of molecular systems and their modeling with bare hands in immersive 3D by multiple concurrent users assisted by on-the-fly molecular mechanics calculations, seamless file exchange, a language model module that interprets users' inputs without them having to learn any menus or scripting, alternative immersive access with consumer devices, and more. With the various tools presented, we offer a glimpse into the present and near future of accessible, interactive, and intuitive molecular science on the web.
Sodium-Glucose Co-Transporter 2 Inhibitors (SGLT2i), initially developed as hypoglycemic agents, have revolutionized the management of cardiorenal diseases due to potent organ-protective effects. Evidence from large clinical trials (DAPA-CKD, EMPA-KIDNEY, DAPA-HF, EMPEROR-Preserved) has established that their renal and cardiac benefits manifest independently of the presence of Type 2 Diabetes Mellitus (T2DM). Renally, nephroprotection mainly stems from the restoration of tubulo-glomerular feedback, which reduces hyperfiltration and intraglomerular pressure, thereby mitigating structural damage in non-diabetic Chronic Kidney Disease (CKD). Cardiologically, SGLT2i improve myocardial metabolism and reduce congestion, demonstrating efficacy in both Heart Failure with reduced ejection fraction (HFrEF) and preserved ejection fraction (HFpEF). This review synthesizes the pleiotropic mechanisms of action and the clinical evidence that extends the use of SGLT2i across the entire spectrum of CKD (eGFR ≥ 20 mL/min/1.73 m2) and Heart Failure offering practical considerations for clinicians regarding dosing and the management of adverse events, such as the initial eGFR dip and the prevention of euglycemic ketoacidosis.
Solid organ transplantation has revolutionized the treatment of end-stage diseases, yet long-term graft survival remains constrained by immune-mediated injury and the limitations of conventional immunosuppression. Among intracellular pathways that translate alloimmune recognition into cellular activation and tissue remodeling, the mechanistic target of rapamycin (mTOR) has emerged as a central regulator of immune cell differentiation, endothelial function, and fibroproliferative responses. Evidence from kidney, heart, liver, and lung transplantation (LTx) implicates dysregulated mTOR signaling in acute cellular and humoral rejection (ACR and AMR) as well as chronic rejection. These observations highlight the importance of understanding molecular mechanisms to refine diagnostics and guide more precise therapeutic strategies targeting mTOR. In LTx, ACR, AMR and chronic rejection converge on shared downstream processes-metabolic reprogramming, endothelial dysfunction, and fibroproliferative remodeling-where mTOR appears pivotal. Pharmacologic inhibitors such as sirolimus and everolimus modulate T- and B-cell activation and limit structural cell proliferation, yet clinical outcomes remain inconsistent, reflecting incomplete knowledge of context-specific mTORC1/2 activation and the lack of validated in vivo biomarkers. Phosphorylated S6 ribosomal protein (p-S6RP) represents a tissue-based readout of mTORC1 activity and a promising biomarker; extending analyses to additional components (p-4EBP1, p-AKT) and integrating multiplex imaging with artificial intelligence could define reproducible "mTOR activation signatures" across cell types and rejection phenotypes. Such biomarker-driven frameworks may enable refined risk stratification and identify patients most likely to benefit from mTOR-targeted therapies. Together, these insights support a shift from empiric immunosuppression toward precision, pathway-guided interventions, positioning mTOR inhibition within a personalized, biology-driven approach to LTx.
Clustered regularly interspaced short palindromic repeats (CRISPR) systems have revolutionized microbial genome editing. However, their reliance on DNA double-strand breaks (DSBs) and homologous recombination limits applications such as large DNA integration and engineering nonmodel microorganisms. CRISPR-associated transposase (CAST) systems provide a promising alternative, enabling DSB-free, recombination-independent, and programmable integration of large DNA fragments. This review summarizes the discovery and characterization of diverse CAST systems, the engineering strategies to enhance integration efficiency and specificity, and their applications in microbial engineering. Current limitations and future directions are also discussed. Overall, CAST systems hold great potential for advancing microbial genome editing, particularly in multi-copy DNA integration and genetic reprogramming of nonmodel microorganisms and complex microbial communities.
Endothelial heterogeneity and plasticity play an important role in lung development, homeostasis, and pathology. In recent years, increasing evidence has demonstrated that endothelial dysfunction contributes to the progression of various lung diseases, such as ADRS, PF, PH, and lung developmental disorders. Therefore, targeting endothelial cells could hold promising therapeutic strategies for preventing disease development. Although significant advances in technology have revolutionized our understanding of endothelial heterogeneity and plasticity, effective and curative treatment options remain limited. Here, we discuss the molecular and functional diversity of lung endothelial cells and their critical role in maintaining lung homeostasis and in lung pathologies. We also briefly describe advanced technologies, such as single-cell RNA sequencing and spatial transcriptomics, to uncover complex cell communication and underlying mechanisms. Furthermore, this review will identify future research questions for developing therapeutic approaches targeting lung endothelial cells.
Gastric cancer (GC) remains a formidable public health challenge in China, characterized by a high incidence and mortality rate. A significant proportion of patients are diagnosed with advanced or metastatic disease, where traditional chemotherapy has historically offered limited survival benefits. However, driven by a deeper molecular understanding of GC heterogeneity, the paradigm of GC treatment is undergoing a revolutionary shift from a "one size fits all" approach toward precision medicine. The identification of predictive biomarkers, such as human epidermal growth factor receptor 2 (HER2/ERBB2; MIM: 164870) expression, microsatellite instability-high (MSI-H) status, PD-L1 (CD274; MIM: 605402) expression, and Claudin 18.2 (CLDN18; MIM: 609210) overexpression, has established biomarker-guided strategies as the unequivocal future direction for managing advanced GC. In particular, the emergence of immune checkpoint inhibitors (ICIs) and agents targeting Claudin 18.2 has revolutionized the landscape of first-line (1L) treatment for GC. China has achieved accelerated breakthroughs in gastric cancer drug research and development, leading to more innovative therapies and improved diagnostic/treatment regimens, providing patients with more abundant and effective treatment options and significantly improving the landscape of diagnosis and treatment. Nevertheless, integrating complex biomarkers and novel agents into practice poses challenges, including inconsistencies in biomarker testing and the need for nuanced treatment regimen selection. Without standardized guidelines, inconsistent application may lead to suboptimal patient outcomes and inefficient use of healthcare resources. This expert consensus has been jointly formulated with the aim of further enhancing and standardizing the biomarker testing and precision medicine of 1L therapy for advanced GC.
RNA sequencing (RNA-Seq) is an advanced technique that enables the comprehensive analysis of gene expression and the transcriptome in biological samples with exceptional precision and scalability. Leveraging platforms like Illumina, PacBio, and Oxford Nanopore, RNA-Seq has revolutionized cancer research by identifying genes, isoforms, and genetic variants. When combined with bioinformatics tools, it allows the detection of gene expression signatures, alternative splicing events, and profiles of non-coding RNAs. Furthermore, single-cell analysis provides insights into tumor heterogeneity, enhancing diagnostics, prognostics, and the development of personalized therapies.Artificial intelligence (AI), particularly explainable AI (XAI), plays a pivotal role in transcriptomic data analysis. Interpretable models, such as regression analyses or decision trees, and post-hoc techniques like LIME and SHAP, improve the reliability and usability of findings by identifying key genes for clinical decision-making. These tools integrate seamlessly with high-resolution single-cell and three-dimensional analyses, exploring intratumoral heterogeneity and cellular signaling pathways.Addressing the heterogeneity of common cancers demands the integration of sequencing technologies and AI. The combination of short- and long-read RNA-Seq enables the identification of isoforms and splicing events critical to cancer biology. Together, these technologies and approaches optimize diagnostic and therapeutic strategies, paving the way for personalized treatments by detecting and characterizing genetic alterations and their impact on the tumor microenvironment.
Spatial technologies have revolutionized the study of human development by enabling molecular profiling within intact tissue architecture. Advances in spatial transcriptomics, epigenomics, proteomics, metabolomics, and multiomics have generated high-resolution atlases of embryonic and fetal organs, which have revealed how gene regulatory programs, cell-cell interactions, and tissue patterning are organized in space and time during organogenesis. However, limited access to human developmental tissues and ethical constraints restrict experimental validation and mechanistic studies. Stem cell-derived organoids offer tractable, human-relevant models that recapitulate key structural and cellular features of developing organs while allowing controlled perturbation and longitudinal analysis. Integrating spatial profiling with organoids constitutes a sophisticated methodology for systematic analysis of lineage specification, niche signaling, and metabolic maturation. This review synthesizes current spatial technologies, highlights emerging organoid-based technologies, and discusses future directions integrating spatial multiomics, machine learning, and accessible data resources to advance mechanistic and predictive developmental biology.