Personalized Treatment Research Articles

State-of-the-Art Liver Cancer Organoids: Modeling Cancer Stem Cell Heterogeneity for Personalized Treatment.

Liver cancer poses a global health challenge with limited therapeutic options. Notably, the limited success of current therapies in patients with primary liver cancers (PLCs) may be attributed to the high heterogeneity of both hepatocellular carcinoma (HCCs) and intrahepatic cholangiocarcinoma (iCCAs). This heterogeneity evolves over time as tumor-initiating stem cells, or cancer stem cells (CSCs), undergo (epi)genetic alterations or encounter microenvironmental changes within the tumor microenvironment. These modifications enable CSCs to exhibit plasticity, differentiating into various resistant tumor cell types. Addressing this challenge requires urgent efforts to develop personalized treatments guided by biomarkers, with a specific focus on targeting CSCs. The lack of effective precision treatments for PLCs is partly due to the scarcity of ex vivo preclinical models that accurately capture the complexity of CSC-related tumors and can predict therapeutic responses. Fortunately, recent advancements in the establishment of patient-derived liver cancer cell lines and organoids have opened new avenues for precision medicine research. Notably, patient-derived organoid (PDO) cultures have demonstrated self-assembly and self-renewal capabilities, retaining essential characteristics of their respective in vivo tissues, including both inter- and intratumoral heterogeneities. The emergence of PDOs derived from PLCs serves as patient avatars, enabling preclinical investigations for patient stratification, screening of anticancer drugs, efficacy testing, and thereby advancing the field of precision medicine. This review offers a comprehensive summary of the advancements in constructing PLC-derived PDO models. Emphasis is placed on the role of CSCs, which not only contribute significantly to the establishment of PDO cultures but also faithfully capture tumor heterogeneity and the ensuing development of therapy resistance. The exploration of PDOs' benefits in personalized medicine research is undertaken, including a discussion of their limitations, particularly in terms of culture conditions, reproducibility, and scalability.

Assessing early therapeutic drug monitoring of adalimumab as a predictor of treatment efficacy and immunogenicity in rheumatic diseases: "early therapeutic drug monitoring of adalimumab".

Therapeutic drug monitoring (TDM) in inflammatory rheumatic diseases (RMDs) is gaining interest. However, there are unresolved questions about the best practices for implementing TDM effectively in clinical settings. The primary objective of this study was to evaluate whether early TDM of adalimumab predicts drug survival at 52 weeks in patients with RMDs. The secondary objective was to identify factors associated with pharmacokinetic failure and treatment discontinuation. A retrospective cohort study included patients aged ≥ 18 years with RMDs who initiated adalimumab therapy. Early TDM was performed within the first 26 weeks, and adalimumab trough levels (ATL) and anti-drug antibodies were measured. Drug survival was assessed at 52 weeks and defined as the time from adalimumab initiation to discontinuation for any reason. Multivariate analyses were conducted to identify factors influencing outcomes. The study included 194 patients, of whom 56.7% exhibited ATL below the therapeutic range during the first 26 weeks. In the multivariate analysis, subtherapeutic concentrations were significantly associated with higher weight (OR = 1.02; p = 0.040) and ankylosing spondylitis diagnosis (OR = 3.68; p < 0.001). At 52 weeks, 43.8% of patients had discontinued adalimumab. Low ATL (< 1 µg/mL) was strongly associated with treatment discontinuation (OR = 7.31; p < 0.001), while concomitant methotrexate reduced this risk (OR = 0.46; p = 0.026). Early TDM of adalimumab predicts drug persistence and underscores its clinical relevance as a proactive tool to guide personalized treatment and reduce the risk of treatment failure. These findings highlight the importance of incorporating TDM into routine practice to optimize therapeutic outcomes. Key Points • Early TDM of adalimumab in rheumatic diseases shows that low drug exposure predicts reduced drug survival at 52 weeks. • Approximately half of the patients exhibit low adalimumab exposure with the standard dose (40 mg every other week). • Body weight and methotrexate use significantly impact adalimumab levels. • Immunogenicity, found in 14.4% of patients with low ADL levels, underscores the need for early ADA detection to prevent non-response and discontinuation.

Reinforcement Learning in Real Life Applications

Reinforcement Learning is a crucial subset of machine learning that enables agents to learn decision-making through interaction with their environments, guided by feedback like rewards or penalties. This paper delves into Reinforcement Learning's applications across diverse sectors such as robotics, finance, healthcare, and gaming. In robotics, Reinforcement Learning facilitates tasks like object manipulation and environmental adaptation. In finance, it aids in portfolio management and trading strategy development, adapting to market dynamics. In healthcare, Reinforcement Learning assists in generating personalized treatment plans and analyzing medical images. Meanwhile, the gaming industry benefits from Reinforcement Learning -driven intelligent agents capable of challenging human players. Throughout, we explore Reinforcement Learning's fundamental principles, core components, and its transformative potential across these real-world applications. Keywords—Reinforcement Learning, Decision-Making, Adaptive Agents.

Endoscopic ultrasonography-based intratumoral and peritumoral machine learning ultrasomics model for predicting the pathological grading of pancreatic neuroendocrine tumors

ObjectivesThe objective is to develop and validate intratumoral and peritumoral ultrasomics models utilizing endoscopic ultrasonography (EUS) to predict pathological grading in pancreatic neuroendocrine tumors (PNETs).MethodsEighty-one patients, including 51 with grade 1 PNETs and 30 with grade 2/3 PNETs, were included in this retrospective study after confirmation through pathological examination. The patients were randomly allocated to the training or test group in a 6:4 ratio. Univariate and multivariate logistic regression were used for screening clinical and ultrasonic characteristics. Ultrasomics is ultrasound-based radiomics. Ultrasomics features were extracted from both the intratumoral and peritumoral regions of conventional EUS images. Subsequently, the dimensionality of these radiomics features was reduced using the least absolute shrinkage and selection operator (LASSO) algorithm. A machine learning algorithm, namely multilayer perception (MLP), was employed to construct prediction models using only the nonzero coefficient features and retained clinical features, respectively.ResultsOne hundred seven ultrasomics features based on EUS were extracted, and only features with nonzero coefficients were ultimately retained. Among all the models, the combined ultrasomics model achieved the greatest performance, with an AUC of 0.858 (95% CI, 0.7512 - 0.9642) in the training group and 0.842 (95% CI, 0.7061 - 0.9785) in the test group. A calibration curve and a decision curve analysis (DCA) also demonstrated its accuracy and utility.ConclusionsThe integrated model using EUS ultrasomics features from intratumoral and peritumoral tumors accurately predicts PNETs' pathological grades pre-surgery, aiding personalized treatment planning.Trial registrationChiCTR2400091906.

Developing a Simple Conceptual Causal Model for Predicting Early Recurrence and Mortality after Curative Surgery for Colorectal Cancer Patients

Introduction: Colorectal cancer (CRC) represents the second leading cause of cancer-related mortality. This study focused on the development of a robust conceptual causal model designed to predict early recurrence and mortality following curative surgery in colorectal cancer patients. Methods: In this retrospective cohort study, we included 284 patients with colorectal cancer (CRC) who underwent surgery at the Imam Khomeini (RA) Clinic in Hamadan, Iran, between 2001 and 2017. Demographic characteristics, treatment modalities, and other relevant data were extracted from patient records. Predictors were analyzed using Generalized Structural Equation Modeling (GSEM) for survival analysis, employing an accelerated failure time (AFT) approach. Both unadjusted and adjusted time ratios (TRs) were calculated using STATA software. Results: The results of our developed causal model indicated that receiving chemotherapy was significantly associated with a shorter survival time ratio (TR = 0.415, 95% CI: 0.290-0.593), and recurrence time (TR = 0.363, 95% CI: 0.190-0.696). Conversely, patients who underwent multiple chemotherapy sessions exhibited a longer survival time (TR = 2.130, 95% CI: 1.790-2.534) and recurrence time (TR = 2.206, 95% CI: 1.609- 3.023). Age had a direct impact on the recurrence time (TR = 0.758, 95% CI: 0.602-0.955). Additionally, age had a significant direct effect on the receipt of chemotherapy, the cancer site, and the receipt of radiotherapy. Conclusion: In summary, our study's causal model reveals that chemotherapy shortens survival time but multiple sessions can extend both survival and recurrence times. Age significantly affects recurrence time and chemotherapy receipt. These findings highlight the importance of personalized treatment strategies in colorectal cancer management

Cancer theragnostics: closing the loop for advanced personalized cancer treatment through the platform integration of therapeutics and diagnostics

Cancer theragnostics: closing the loop for advanced personalized cancer treatment through the platform integration of therapeutics and diagnostics

Exploration of an adaptive proton therapy strategy using CBCT with the concept of digital twins

Objective.This study aims to develop a digital twin (DT) framework to achieve adaptive proton prostate stereotactic body radiation therapy (SBRT) with fast treatment plan selection and patient-specific clinical target volume (CTV) setup uncertainty. Prostate SBRT has emerged as a leading option for external beam radiotherapy due to its effectiveness and reduced treatment duration. However, interfractional anatomy variations can impact treatment outcomes. This study seeks to address these uncertainties using DT concept to improve treatment quality.Approach. A retrospective study on two-fraction prostate proton SBRT was conducted, involving a cohort of 10 randomly selected patient cases from an institutional database (n= 43). DT-based treatment plans were developed using patient-specific CTV setup uncertainty, determined through machine learning predictions. Plans were optimized using pre-treatment CT and corrected cone-beam CT (cCBCT). The cCBCT was corrected for CT numbers and artifacts, and plan evaluation was performed using cCBCT to account for actual patient anatomy. The ProKnow scoring system was adapted to determine the optimal treatment plans.Main Results.Average CTV D98 values for original clinical and DT-based plans across 10 patients were 99.0% and 98.8%, with hot spots measuring 106.0% and 105.1%. Regarding bladder, clinical plans yielded average bladder neck V100 values of 29.6% and bladder V20.8 Gy values of 12.0cc, whereas DT-based plans showed better sparing of bladder neck with values of 14.0% and 9.5cc. Clinical and DT-based plans resulted in comparable rectum dose statistics due to SpaceOAR. Compared to clinical plans, the proposed DT-based plans improved dosimetry quality, improving plan scores ranging from 2.0 to 15.5.Significance.Our study presented a pioneering approach that leverages DT technology to enhance adaptive proton SBRT, potentially revolutionizing prostate radiotherapy to offer personalized treatment solutions using fast adaptive treatment plan selections and patient-specific setup uncertainty. This research contributes to the ongoing efforts to achieve personalized prostate radiotherapy.

Towards Personalized Radiotherapy in Pelvic Cancer: Patient-Related Risk Factors for Late Radiation Toxicity

Normal tissue reactions vary significantly among patients receiving the same radiation treatment regimen, reflecting the multifactorial etiology of late radiation toxicity. Predicting late radiation toxicity is crucial, as it aids in the initial decision-making process regarding the treatment modalities. For patients undergoing radiotherapy, anticipating late toxicity allows for planning adjustments to optimize individualized care. Various dosimetric parameters have been shown to influence the incidence of late toxicity, and the literature available on this topic is extensive. This narrative review examines patient-related determinants of late toxicity following external beam radiotherapy for pelvic tumors, with a focus on prostate and cervical cancer patients. In Part I, we address various methods for quantifying radiation toxicity, providing context for interpreting toxicity data. Part II examines the current insights into the clinical risk factors for late toxicity. While certain factors—such as previous abdominal surgery, smoking behavior, and severe acute toxicity—have consistently been reported, most of the others show inconsistent associations. In Part III, we explore the influence of genetic factors and discuss promising predictive assays. Single-nucleotide polymorphisms (SNPs) likely elevate the risk in specific combinations. Advances in artificial intelligence now allow for the identification of SNP patterns from large datasets, supporting the development of polygenic risk scores. These innovations hold promise for improving personalized treatment strategies and reducing the burden of late toxicity in cancer survivors.

Expression Profile of MicroRNAs in Breast Milk of Women With Inflammatory Bowel Disease: Correlation With Disease Activity and Medical Treatments.

Although most inflammatory bowel disease (IBD) medications are considered safe during pregnancy, their impact on microRNAs (miRNAs) in breast milk is largely unknown. MiRNAs in milk, carried by milk-derived extracellular vesicles (MDEs), are transmitted to the newborn's gut to regulate genes. Aberrant miRNA expression profiles have been found in IBD within tissue, blood, and feces, but data on mother's milk are scarce. We collected breast milk samples from 32 mothers with Crohn's disease (CD), 14 mothers with ulcerative colitis (UC), and 44 healthy controls. We analyzed miRNA expression through qualitative real-time polymerase chain reaction and Affymetrix miRNA chips. Target genes of differentially expressed miRNAs were predicted using miRATBase. Statistical analyses were conducted using GraphPad Prism software with Mann-Whitney tests. Milk-derived extracellular vesicles from mothers with IBD showed altered miRNA profiles compared to controls. Specifically, miR-21 and miR-320 were downregulated, while Let-7a was upregulated in IBD mothers. The expression patterns varied between CD and UC, with significantly lower MiR-21 in UC and higher Let-7a in CD. Additionally, anti-tumor necrosis factor treatment during pregnancy was associated with reduced miR-21 and miR-148a levels in MDEs. Pathway analysis revealed that these miRNAs are involved in immune regulation, particularly interleukin signaling pathways. This study highlights that miRNAs in breast milk are differentially expressed in mothers with IBD, influenced by the disease and its treatments. These findings emphasize the impact of maternal health on milk composition and potential implications for infant immune development. Understanding these findings may guide personalized treatment strategies for mothers and promote breastfeeding among mothers with IBD.

Characterization of diabetic kidney disease in 235 patients: clinical and pathological insights with or without concurrent non-diabetic kidney disease

BackgroundThis study aimed to explore the clinical and pathological features of patients with diabetic kidney disease (DKD), with and without non-diabetic kidney disease (NDKD), through a retrospective analysis. The objective was to provide clinical insights for accurate identification.MethodsA retrospective analysis of 235 patients admitted to the Department of Nephrology at Hangzhou Hospital of Traditional Chinese Medicine was conducted between July 2014 and December 2022. These patients underwent renal biopsy and received a pathology-based diagnosis of DKD. They were categorized into the DKD alone group (93 cases) and the DKD + NDKD group (142 cases).ResultsIn the DKD alone group, gender distribution was even, with ages mainly between 50 and 59 years, and a disease duration of less than 5 years, primarily presenting nodular diabetic glomerulosclerosis. In contrast, the DKD + NDKD group had a higher male incidence, a wider age range, longer disease duration, and prevalent diffuse diabetic glomerulosclerosis. Acute and chronic tubulointerstitial lesions and IgA nephropathy were the predominant types of combined NDKD, accounting for 40.14% and 35.21%, respectively. Clinical correlation analysis revealed associations between glomerular grading, tubulointerstitial lesions, renal arteriolar vitelliform lesions, renal vascular atherosclerosis, and clinical parameters such as 24-hour urine protein, hemoglobin, and urinary specific gravity. Multifactorial logistic regression analysis identified independent factors affecting DKD + NDKD, including body mass index, blood creatinine level, microscopic erythrocyte grade, urinary immunoglobulin G/creatinine ratio, and serum immunoglobulin A.ConclusionThe research underscores distinctions in age, gender distribution, disease duration, and renal pathology between DKD alone and DKD + NDKD groups. Additionally, significant discriminative factors including BMI, blood creatinine level, microscopic erythrocyte grade, UIgG/urine creatinine ratio, and serum IgA levels help differentiate DKD from NDKD, thereby enabling personalized treatment approaches. Furthermore, the study highlights the role of RASi as the most commonly used drug in the treatment of both DKD and NDKD, with emerging drugs such as SGLT2 inhibitors showing promising renal protective effects.

Establishment and Validation of the Diagnostic Value of Oligodendrocyte-related Genes in Alzheimer's Disease.

AD is a demyelinating disease. Myelin damage initiates the pathological process of AD, resulting in abnormal synaptic function and cognitive decline. The myelin sheath formed by oligodendrocytes (OL) is a crucial component of white matter. Investigating AD from the perspective of OL may offer novel diagnostic and therapeutic perspectives. This study aimed to analyze the association between OL-related genes and Alzheimer's disease (AD) using bioinformatics and verify this association via molecular biology experiments. The AD datasets were acquired from the Gene Expression Omnibus (GEO) database of NCBI. Consensus clustering was employed to determine the subtypes of AD, followed by evaluating the clinical characteristics of these subtypes. Subsequently, immune infiltration analysis of relevant genes and Weighted Gene Co-expression Network Analysis (WGCNA) were conducted to identify modules and hub genes associated with AD progression. The intersection of genes obtained was analyzed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. To narrow down the scope and identify OL-related genes with diagnostic potential, three machine learning algorithms were utilized. In addition, the eXtreme Sum (XSum) algorithm was used to screen small molecule drug candidates based on the connectivity map (CMAP) database. Finally, these identified genes were validated using Real-time fluorescence quantitative PCR (RT-qPCR). Among the three subtypes of AD, Cluster A and Cluster C exhibited increased levels of Braak and neurofibrillary tangles compared to Cluster B. The proportion of females was greater than that of males among the three subclasses of AD. There were no significant differences in age among the three subclasses, but significant differences in gene expression existed. Through WGCNA analysis, 108 genes were identified. Among these, 16 genes were identified as shared genes by the least absolute shrinkage and selection operator (LASSO), random forest (RF), and support vector machines (SVM) algorithms, and logistic regression further determined 11 genes. The establishment of a nomogram demonstrated the significance of these 11 genes in AD. The "XSum" algorithm revealed five drugs with therapeutic potential for AD. qPCR analysis revealed the upregulation and downregulation of the highlighted genes. According to this study, 11 genes related to OL were also found to be associated with immune cell infiltration in AD patients. These genes demonstrated potential diagnostic value for AD. Additionally, we screened five small molecular drugs that exhibit potential therapeutic effects on AD. This research provides a new perspective for personalized clinical management and treatment of AD.

Population pharmacokinetic model of tranexamic acid in patients who undergo cardiac surgery with cardiopulmonary bypass.

Tranexamic acid (TXA) is widely used as an antifibrinolytic drug. However, studies to determine the optimal blood concentration of TXA have produced inconsistent results. During cardiac surgery, cardiopulmonary bypass (CPB) has serious effects on drug distribution, elimination, and plasma concentration. Therefore, we aimed to establish a population pharmacokinetics model of TXA in patients undergoing cardiac surgery with CPB that considers renal function as a covariate, thereby facilitating personalized treatment. In total, 453 TXA plasma samples were prospectively collected from 77 patients who underwent cardiac surgery with CPB. Plasma concentrations were determined by ultra-performance liquid chromatography-tandem mass spectrometry. The population pharmacokinetic model of TXA was analyzed using nonlinear mixed-effects modeling. The two-compartment-based model with combined errors was determined as the best. The final model included the effect of bodyweight and CLcr may be summarized as V1 (L) = 12.77 × (bodyweight / 61.4)0.911, V2 (L) = 6.857, CL1 (L/h) = 3.263 × [CLcr (L/h) / 61.0]0.752, CL2 (L/h) = 2.859. Patients who undergo cardiac surgery with CPB may require an adjusted dose of TXA tailored to CPB due to lower CL1 and increased V1. Our TXA population pharmacokinetic model may be useful for developing individualized dosing designs for TXA in patients who undergo cardiac surgery with CPB.

Neuropsychiatric symptoms predict rate of change in executive function in Alzheimer's disease and related dementias.

Neuropsychiatric symptoms (NPS) are considered diagnostic and prognostic indicators of dementia and are attributable to neurodegenerative processes. Little is known about the prognostic value of early NPS on executive functioning (EF) decline in Alzheimer's disease and related dementias (ADRD). We examined whether baseline NPS predicted the rate of executive function (EF) decline among older adults with ADRD. Older adults (n = 1625) with cognitive impairment were selected from the National Alzheimer's Coordinating Center database. EF was estimated with a latent factor indicated by scores on Number Span Backward, Letter Fluency, and Trail Making-Part B. A curve of factors (CUFF) latent growth curve model was estimated to examine rate of change over four years. Baseline NPS severity was entered as a predictor in the model to examine its influence on the rate of change in EF over time. The CUFF models exhibited good fit. EF significantly declined over four waves (slope = -.16, p < .001). Initial visit NPS severity predicted decline in EF (slope = .013, p < .001), such that those with greater baseline NPS severity demonstrated a more rapid decline in EF performance over time. Presence of 2 NPS significantly predicted EF decline, and those with medium total NPS severity (NPS score of 2-4) at baseline exhibited a sharper decline in EF. Findings underscore the importance of targeting NPS early across ADRD syndromes to minimize EF decline, offering novel insights into how early NPS treatment may alter cognitive trajectories. We provide an innovative, user-friendly web-based application that may be helpful for personalized treatment planning.

Comment on 'The risk of malignancy in patients with psoriasis treated with long-term tumor necrosis factor-α inhibitor: a systematic review and meta-analysis' - Risk of Cancer Recurrence with Biologics in Psoriasis.

This article reviews the findings of Wu et al. on the risk of malignancy in psoriasis patients treated with long-term TNF-α inhibitors (TNFi). While the study found no significant increase in the risk of most cancers, it highlighted a higher risk of non-melanoma skin cancers, particularly squamous cell carcinoma, in patients with psoriatic arthritis. The article discusses the clinical implications for psoriasis patients with a history of malignancy, advocating for a personalised treatment approach, vigilant monitoring, and the need for further research to better understand the relationship between biologic therapy and cancer recurrence.

Generative Artificial Intelligence Use in Healthcare: Opportunities for Clinical Excellence and Administrative Efficiency

Generative Artificial Intelligence (Gen AI) has transformative potential in healthcare to enhance patient care, personalize treatment options, train healthcare professionals, and advance medical research. This paper examines various clinical and non-clinical applications of Gen AI. In clinical settings, Gen AI supports the creation of customized treatment plans, generation of synthetic data, analysis of medical images, nursing workflow management, risk prediction, pandemic preparedness, and population health management. By automating administrative tasks such as medical documentations, Gen AI has the potential to reduce clinician burnout, freeing more time for direct patient care. Furthermore, application of Gen AI may enhance surgical outcomes by providing real-time feedback and automation of certain tasks in operating rooms. The generation of synthetic data opens new avenues for model training for diseases and simulation, enhancing research capabilities and improving predictive accuracy. In non-clinical contexts, Gen AI improves medical education, public relations, revenue cycle management, healthcare marketing etc. Its capacity for continuous learning and adaptation enables it to drive ongoing improvements in clinical and operational efficiencies, making healthcare delivery more proactive, predictive, and precise.

Characterization of the genomic landscape in liver oligometastatic NSCLC

ObjectivesEmerging data have shown that local treatment could provide clinical benefit for non-small cell lung cancer (NSCLC) patients with oligometastasis. Liver metastases have the worst prognosis in advanced NSCLC, but the genomic characteristics of liver oligometastasis remain unclear. The aim of our study was to elucidate the molecular features of liver oligometastatic NSCLC.MethodsPaired liver metastatic tissue samples and peripheral blood from 32 liver oligometastatic NSCLC patients were concurrently collected for comprehensive genomic analysis using next-generation sequencing.ResultsA total of 206 mutated genes in 32 patients were detected, with a median of 4 mutations per sample. The most frequent alterations (> 10%) in liver oligometastasis were TP53 (72%), EGFR (50%), RB1 (19%) and SMARCA4 (12%). The co-occurrence rate of TP53 and RB1 in our cohort was significantly higher than that in the TCGA-LUAD cohort. Age, APOBEC, homologous recombination deficiency (HRD) and deficient mismatch repair (dMMR) established the mutational signature of liver oligometastatic NSCLC. The median tumor mutation burden (TMB) was 4.8 mutations/Mb. A total of 78.12% patients harbored at least one potentially actionable molecular alteration that may guide further targeted therapy according to the OncoKB evidence.ConclusionsOur study comprehensively delineated the genomic characteristics of liver oligometastatic NSCLC - such findings were helpful to better understand the distinct clinic-biological features of oligometastasis and optimize personalized treatment of this population.

A Facile Approach To Develop Ion Pair Micelles Satellited Freshly Derived Neutrophils For Targeted Tumor Therapy.

Immune cells show enormous potential for targeted nanoparticle delivery due to their intrinsic tumor-homing skills. However, the immune cells can internalize the nanoparticles, leading to cellular functional impairments, degradation of the nanoparticles, and delayed release of drugs from the immune cells. To address these issues, this study introduces an approach for the synthesis of freshly derived neutrophils (NUs)-based nanocarriers system where the NUs are surfaced by dialdehyde alginate-coated self-assembled micelles loaded with mitoxantrone (MIT) and indocyanine green (ICG) (i.e., dA(MI@IPM)s) for stimuli-responsive tumor-targeted therapy. Here, the dA(MI@IPM)s are not internalized by the NUs, but they are anchored on the membrane of the NUs via distearoylphosphatidylethanolamine-polyethylene glycol-polyethylenimine anchors. Owing to the natural recruitment ability of NUs to the tumor microenvironment, NUs-anchored dA(MI@IPM)s accumulation is higher at the tumor site than free dA(MI@IPM)s, where the dA(MI@IPM)s can readily detach from the NUs to get internalized in the tumor cells. The stimuli-responsive dA(MI@IPM)s disassembles inside the cancer cells upon near-infrared irradiation due to the photosensitizing effect of the loaded ICG, releasing MIT and significantly inhibiting tumor growth. This approach is simple and fast to prepare, opening up exciting possibilities for personalized cancer treatment using patient's autologous NUs.

Genetic Mutation and Regulation in Pancreatic Ductal Adenocarcinoma Cancer: Developing Relevant Molecular Biomarkers

Pancreatic ductal adenocarcinoma (PDAC) is a highly invasive and highly lethal malignant tumor. Due to the difficulty in early diagnosis and limited treatment options, the 5-year survival rate is less than 10%. Traditional treatments such as surgery, chemotherapy, and radiotherapy have limited effects on PDAC, which is mainly attributed to its unique tumor microenvironment (TME), common gene mutations (such as KRAS, and TP53), and immunosuppressive properties. In recent years, with the advancement of molecular biology and genomics technologies, the molecular mechanisms of PDAC have been studied more deeply, revealing the gene mutations and regulatory networks associated with its pathogenesis. In particular, KRAS mutations have become an important research direction for targeted therapy. However, the effect of immunotherapy in PDAC is limited by the immune escape characteristics of TME. This article systematically summarizes the key gene mutations in PDAC and their regulatory mechanisms, including epigenetic regulation, microRNA, histone modification and other influencing factors. CRISPR-Cas9 shows great potential in correcting gene mutations and reshaping TME. In addition, combining immunotherapy with targeted drug delivery (such as nanotechnology) can improve the precision of treatment and reduce side effects. These advances provide a comprehensive reference for future personalized and effective treatment options for PDAC

Nanotechnology-Based Targeted Drug Delivery Systems for Cancer Treatment

This study explores the mechanism of nanotechnology-based targeted drug delivery in cancer treatment, particularly focusing on the preparation and application of nanodrugs for specific types of cancer. The article first analyzes the physicochemical properties and drug delivery advantages of common carriers such as liposomes, gold nanoparticles, and polysaccharide nanoparticles. It then discusses the classification, preparation, and application of nanoparticle-based delivery systems and nanoemulsion-based delivery systems. Following this, the drug delivery mechanisms of nanodelivery systems are examined, particularly how pH, temperature, light, or magnetic field-responsive nanodelivery systems enable targeted release. Furthermore, this paper focuses on the compatibility of combined drugs, the stability of the mixture, and potential toxic side effects. Finally, while enhancing the therapeutic efficiency of drugs, nanotechnology-based targeted drug delivery systems offer new approaches for personalized cancer treatment and provide a theoretical foundation and reference for the future development of such systems.

Opportunities and limitations of B cell depletion approaches in SLE.

B cell depletion with rituximab, a chimeric monoclonal antibody that selectively targets B cells by binding CD20, has been used offlabel in severe and resistant systemic lupus erythematosus (SLE) for over two decades. Several biological mechanisms limit the efficacy of rituximab, including immunological reactions towards the chimeric molecule, increased numbers of residual B cells, including plasmablasts and plasma cells, and a post-treatment surge in B cell-activating factor (BAFF) levels. Consequently, rituximab induces remission in only a proportion of patients, and safety issues limit its use. However, the use of rituximab has established the value of B cell depletion strategies in SLE and hasguided the development of several improved B cell depletion therapies for SLE. These include enhanced monoclonal antibodies, modalities that redirect the specificity of patient T cells using chimeric antigen receptor T cells or bispecific T cell engagers, and combination treatment that simultaneously inhibits the BAFF pathway. In this Review, we consider evidence gathered from over two decades of using rituximab in SLE and examine how B cell depletion therapies could be further optimized to achieve immunological and clinical efficacy. In addition, we discuss the prospects of B cell depletion strategies for personalized treatment in SLE based on genetic research and studies in pre-symptomatic individuals.