Visibility of esophageal squamous cell carcinoma under iodine staining on texture and color enhancement imaging.

Objective: Iodine staining on white light imaging (WLI) is the gold standard for detecting and demarcating esophageal squamous cell carcinoma (ESCC). We examined the effects of texture and color enhancement imaging (TXI) on improving the endoscopic visibility of ESCC under iodine staining. Methods: Twenty ESCC lesions that underwent endoscopic submucosal dissection were retrospectively included. The color difference between ESCC and the surrounding mucosa (ΔEe) on WLI, TXI, and narrow-band imaging was assessed, and ΔEe under 1% iodine staining on WLI and TXI. Furthermore, the visibility grade determined by endoscopists was evaluated on each imaging. Result: The median ΔEe was greater on TXI than on WLI (14.53 vs. 10.71, respectively; p < 0.005). Moreover, the median ΔEe on TXI under iodine staining was greater than the median ΔEe on TXI and narrow-band imaging (39.20 vs. 14.53 vs. 16.42, respectively; p < 0.005 for both). A positive correlation in ΔEe under iodine staining was found between TXI and WLI (correlation coefficient = 0.61, p < 0.01). Moreover, ΔEe under iodine staining on TXI in each lesion was greater than the corresponding ΔEe on WLI. The visibility grade assessed by endoscopists on TXI was also significantly greater than that on WLI under iodine staining (p < 0.01). Conclusions: The visibility of ESCC after iodine staining was greater on TXI than on WLI.

Multi-omics differences in the bone marrow between essential thrombocythemia and prefibrotic primary myelofibrosis.

Essential thrombocythemia (ET) and prefibrotic primary myelofibrosis (pre-PMF) are Philadelphia chromosome-negative myeloproliferative neoplasms. These conditions share overlapping clinical presentations; however, their prognoses differ significantly. Current morphological diagnostic methods lack reliability in subtype differentiation, underlining the need for improved diagnostics. The aim of this study was to investigate the multi-omics alterations in bone marrow biopsies of patients with ET and pre-PMF to improve our understanding of the nuanced diagnostic characteristics of both diseases. We performed proteomic analysis with 4D direct data-independent acquisition and microbiome analysis with 2bRAD-M sequencing technology to identify differential protein and microbe levels between untreated patients with ET and pre-PMF. Laboratory and multi-omics differences were observed between ET and pre-PMF, encompassing diverse pathways, such as lipid metabolism and immune response. The pre-PMF group showed an increased neutrophil-to-lymphocyte ratio and decreased high-density lipoprotein and cholesterol levels. Protein analysis revealed significantly higher CXCR2, CXCR4, and MX1 levels in pre-PMF, while APOC3, APOA4, FABP4, C5, and CFB levels were elevated in ET, with diagnostic accuracy indicated by AUC values ranging from 0.786 to 0.881. Microbiome assessment identified increased levels of Mycobacterium, Xanthobacter, and L1I39 in pre-PMF, whereas Sphingomonas, Brevibacillus, and Pseudomonas_E were significantly decreased, with AUCs for these genera ranging from 0.833 to 0.929. Our study provides preliminary insights into the proteomic and microbiome variations in the bone marrow of patients with ET and pre-PMF, identifying specific proteins and bacterial genera that warrant further investigation as potential diagnostic indicators. These observations contribute to our evolving understanding of the multi-omics variations and possible mechanisms underlying ET and pre-PMF.

Special articleFear of illness in Kafka's Metamorphosis.

This year marks 100 years since the death of Franz Kafka. Often in general medicine, and internal medicine in particular, doctors face situations in which they position themselves as the only guarantor of the patient in relation to society and how it conceives the disease. Many times, patients come to us without a diagnosis or with the fear of it; sometimes also rejected by their environment. This short text addresses this current topic, paying tribute to the brilliant writer and his best-known work, Metamorphosis.

An analytical review on the use of artificial intelligence and machine learning in diagnosis, prediction, and risk factor analysis of multiple sclerosis.

Medical research offers potential for disease prediction, like Multiple Sclerosis (MS). This neurological disorder damages nerve cell sheaths, with treatments focusing on symptom relief. Manual MS detection is time-consuming and error prone. Though MS lesion detection has been studied, limited attention has been paid to clinical analysis and computational risk factor prediction. Artificial intelligence (AI) techniques and Machine Learning (ML) methods offer accurate and effective alternatives to mapping MS progression. However, there are challenges in accessing clinical data and interdisciplinary collaboration. By analyzing 103 papers, we recognize the trends, strengths and weaknesses of AI, ML, and statistical methods applied to MS diagnosis. AI/ML-based approaches are suggested to identify MS risk factors, select significant MS features, and improve the diagnostic accuracy, such as Rule-based Fuzzy Logic (RBFL), Adaptive Fuzzy Inference System (ANFIS), Artificial Neural Network methods (ANN), Support Vector Machine (SVM), and Bayesian Networks (BNs). Meanwhile, applications of the Expanded Disability Status Scale (EDSS) and Magnetic Resonance Imaging (MRI) can enhance MS diagnostic accuracy. By examining established risk factors like obesity, smoking, and education, some research tackled the issue of disease progression. The performance metrics varied across different aspects of MS studies: Diagnosis: Sensitivity ranged from 60 % to 98 %, specificity from 60 % to 98 %, and accuracy from 61 % to 97 %. Prediction: Sensitivity ranged from 76 % to 98 %, specificity from 65 % to 98 %, and accuracy from 62 % to 99 %. Segmentation: Accuracy ranged up to 96.7 %. Classification: Sensitivity ranged from 78 % to 97.34 %, specificity from 65 % to 99.32 %, and accuracy from 71 % to 97.94 %. Furthermore, the literature shows that combining techniques can improve efficiency, exploiting their strengths for better overall performance.

Prenatal diagnostic and intervention considerations in congenital diaphragmatic hernia.

Congenital diaphragmatic hernia (CDH) is a life-threatening birth defect with significant morbidity and mortality. The prenatal management of a pregnancy with a fetus affected with CDH is complex and requires a multi-disciplinary team approach. An improved understanding of prenatal diagnosis and management is essential to developing strategies to optimize outcomes for these patients. In this review, we explore the current knowledge on diagnosis, severity stratification, prognostic prediction, and indications for fetal intervention in the fetus with CDH.

Prevalence of HPV16 L1 protein in oral biopsies: A diagnostic study from Ecuador.

This study was designed to investigate the expression of HPV16 L1-protein in biopsies of oral mucosa samples. The expression of HPV16 L1 protein was investigated in biopsies taken from oral mucosa from patients who required pathological diagnosis of oral lesions. Seventy-two samples were incubated with anti-L1 protein monoclonal antibodies and protein detection was revealed with diaminobenzidine. Expression of L1 protein was performed by a pathologist blinded for tissue diagnosis under light microscopy. Most of the lesions of oral mucosa were present in lining mucosa (75 %) and the most frequent lesion were mucocele (n = 17, 23.6 %), epithelial hyperplasia (n = 6, 8.33 %), fibroma (n = 5, 6.9 %) and inflammatory hyperplasia (n = 5, 6.9 %). L1 protein expression was observed only in five (6.9 %) samples (two squamous cell carcinomas, two epithelial hyperplasia, and one gingival hyperplasia). We concluded that L1 expression in oral biopsies presented a low frequency in oral mucosal biopsies samples.

Smell cancer by machine learning-assisted peptide/MXene bioelectronic array.

Non-invasive detection of tumors is of utmost importance to save lives. Nonetheless, identifying tumors through gas analysis is a challenging task. In this work, biosensors with remarkable gas-sensing characteristics were developed using a self-assembly method consisting of peptides and MXene. Based on these biosensors, a mimetic biosensor array (MBA) was fabricated and integrated into a real-time testing platform (RTP). In addition, machine learning (ML) algorithms were introduced to improve the RTP's detection and identification capabilities of exhaled gas signals. The synthesized biosensor, with the ability to specifically bind to targeted gas molecules, demonstrated higher performance than the pristine MXene, with a response up to 150% greater. Besides, the MBA successfully detected 15 odor molecules affiliated with five categories of alcohols, ketones, aldehydes, esters, and acids by pattern recognition algorithms. Furthermore, with the ML assistance, the RTP detected the breath odor samples from volunteers of four categories, including healthy populations, patients of lung cancer, upper digestive tract cancer, and lower digestive tract cancer, with accuracies of 100%, 94.1%, 90%, and 95.2%, respectively. In summary, we have developed a cost-effective and precise model for non-invasive tumor diagnosis. Furthermore, this prototype also offers a versatile solution for diagnosing other diseases like nephropathy, diabetes, etc.

Managing A Patient with Uveitis in The Era of Artificial Intelligence: Current Approaches, Emerging Trends, And Future Perspectives.

The integration of artificial intelligence (AI) with healthcare has opened new avenues for diagnosing, treating, and managing medical conditions with remarkable precision. Uveitis, a diverse group of rare eye conditions characterized by inflammation of the uveal tract, exemplifies the complexities in ophthalmology due to its varied causes, clinical presentations, and responses to treatments. Uveitis, if not managed promptly and effectively, can lead to significant visual impairment. However, its management requires specialized knowledge, which is often lacking, particularly in regions with limited access to care. AI's capabilities in pattern recognition, data analysis, and predictive modelling offer significant potential to revolutionize uveitis management. AI can classify disease outcomes, analyze multimodal imaging data, and identify new therapeutic targets. However, transforming these AI models into clinical applications and meeting patient expectations involves overcoming challenges like acquiring extensive, annotated datasets, ensuring algorithmic transparency, and validating these models in real-world settings. This review delves into the complexities of uveitis and the current AI landscape, discussing the development, opportunities, and challenges of AI from theoretical models to bedside application. It also examines the epidemiology of uveitis, the global shortage of uveitis specialists, and the disease's socioeconomic impacts, underlining the critical need for AI-driven approaches. Furthermore, it explores the integration of AI in diagnostic imaging and future directions in ophthalmology, aiming to highlight emerging trends that could transform management of a patient with uveitis and suggesting collaborative efforts to enhance AI applications in clinical practice.

Brain gliomas: Diagnostic and therapeutic issues and the prospects of drug-targeted Nano-delivery technology.

Glioma is the most common intracranial malignant tumor, with severe difficulty in treatment and a low patient survival rate. Due to the heterogeneity and invasiveness of tumors, lack of personalized clinical treatment design, and physiological barriers, it is often difficult to accurately distinguish gliomas, which dramatically affects the subsequent diagnosis, imaging treatment, and prognosis. Fortunately, nano-delivery systems have demonstrated unprecedented capabilities in diagnosing and treating gliomas in recent years. They have been modified and surface modified to efficiently traverse BBB/BBTB, target lesion sites, and intelligently release therapeutic or contrast agents, thereby achieving precise imaging and treatment. In this review, we focus on nano-delivery systems. Firstly, we provide an overview of the standard and emerging diagnostic and treatment technologies for glioma in clinical practice. After induction and analysis, we focus on summarizing the delivery methods of drug delivery systems, the design of nanoparticles, and their new advances in glioma imaging and treatment in recent years. Finally, we discussed the prospects and potential challenges of drug-delivery systems in diagnosing and treating glioma.

The ratio of bone marrow myeloid progenitor cell proportion to mature lymphocytes proportion can effectively differentiate aplastic anemia and hypoplastic myelodysplastic syndrome and evaluate the quality of bone marrow aspirates.

INTRODUCTION: Aplastic anemia (AA) and hypoplastic myelodysplastic syndrome (MDS-h) are bone marrow failure disease and difficult to distinguish merely by morphological analysis. In this study, we investigated the value of flow cytometry (FCM) in the differential diagnosis of AA and MDS-h. METHODS: We included 822 patients (626 control, 69 AA, 22 MDS-h and 105 dilution patients) from January 2017 to December 2022 for a retrospective study. Bone marrow myeloid progenitor (MP) cell and mature lymphocytes proportions were analyzed by FCM. The ratio of MP cell proportion and mature lymphocytes proportion, MPLR, was calculated. Data were compared by Kruskal-Wallis test. Differential diagnostic efficacy was evaluated by receiver operating characteristic (ROC) curve. Cutoff value was determined by the maximum Youden index. RESULTS: Bone marrow MP cell proportion and MPLR of MDS-h patients were higher than AA patients. Mature lymphocytes proportion of MDS-h patients was lower than AA patients. Area under ROC curve (AUC of ROC) of MP cell proportion, MPLR and mature lymphocytes proportion to distinguish AA from MDS-h were 0.992, 0.988, and 0.850, respectively. Moreover, MPLR of dilution patients was higher than AA patients but lower than MDS-h patients. The AUC of ROC curves of MPLR to distinguish MDS-h and AA from dilution were 0.854 and 0.871, respectively. CONCLUSION: Bone marrow MP cell proportion and MPLR can effectively discriminate AA from MDS-h with similar differential efficacy, which is higher than mature lymphocytes proportion. Moreover, MPLR can evaluate the quality of bone marrow aspirates, which would interfere with the differential diagnosis.

Immunoinformatic approaches for ErpY-LemA chimeric protein design for use in leptospirosis control.

AIMS: Currently, immunoinformatic approaches have shown promise in rapidly and cost-effectively identifying new antigens from the Leptospira proteome. Chimeric multiepitope proteins offer a strategy with significant potential for implementation in serology and the design of effective vaccines. METHODS AND RESULTS: In this study, we detail the immunoinformatic analyses and design of a new recombinant chimeric protein constructed with epitopes identified from the sequences of ErpY-like and LemA proteins, previously identified as potential antigens for controlling leptospirosis. We expressed the chimeric protein using Escherichia coli heterologous systems, evaluated its antigenicity using serum from naturally infected patients, and its immunogenicity in mice as an animal model, with Freund as an adjuvant. The resulting recombinant chimeric protein, named rErpY-LemA, was successfully expressed and purified using a prokaryotic system, with an expected mass of 35 kDa. Serologic assays using serum samples from naturally infected patients demonstrated recognition of the chimera protein by antibodies present in sera. Animals immunized with the chimera exhibited a significant IgG antibody response from the 7th day (P<0.001), persisting until day 49 of experimentation, with a titer of 1:12,800 (P<0.05). Notably, significant production of IgA, IgM, and IgG subclasses was observed in animals immunized with the chimera. CONCLUSIONS: These results highlight the promising role of immunoinformatics in rapidly identifying antigens and the potential of chimeric multiepitope proteins in developing effective strategies for leptospirosis control.

From theory to practice: Implementing next-generation sequencing and public health genomics in healthcare systems.

If Europe's health systems make a conscious decision to increase their utilization of technology and techniques that can enhance prevention and expedite early-stage diagnosis, they can effectively address the growing challenges of disease. By embracing these advancements, these health systems can significantly improve their response to emerging health issues.However, at present the effective integration and exploitation of these opportunities remains hesitant and suboptimal, and health and health services underperform accordingly, with patients suffering from the continuing variations in diagnosis and access to innovation. This paper presents a comprehensive study that examines the current state of various influential disciplines and factors in European countries. It specifically focuses on the adoption of Next Generation Screening technologies and the development stage of Public Health Genomics. The assessment of these areas is presented in the context of a rapidly changing policy environment, which provides an opportunity for a fundamental reconsideration of how and where new tools can be integrated into healthcare systems and routine practices. Top of Form.

Probe-guided endoscopic system for 5-aminolevulinic acid-based photodynamic diagnosis in cholangiocarcinoma.

BACKGROUND AND AIM: The diagnostic accuracy for cholangiocarcinoma (CCA) is inadequate, necessitating the exploration of novel diagnostic approaches. Protoporphyrin IX (Pp IX), a metabolic product of 5-aminolevulinic acid (5-ALA), emits red fluorescence upon blue light exposure. Because it accumulates selectively in cancer cells, photodynamic diagnosis using 5-ALA (5-ALA-PDD) has been integrated into clinical practice for diverse cancer types. Nevertheless, there is currently no device capable of capturing Pp IX-derived fluorescence for real-time 5-ALA-PDD within the biliary tract, largely due to challenges in device miniaturization. METHODS: To investigate the feasibility of real-time 5ALA-PDD in CCA, we developed two essential components of the cholangioscopy system: a small-diameter flexible camera and a light guide for emitting blue light. We evaluated the detectability of Pp IX fluorescence using these devices in experimental gels and animal models. RESULTS: Our camera and light guide were smoothly inserted into the lumen of existing cholangioscopes. Incorporating a long-pass filter at the camera tip enabled efficient detection of red fluorescence without significantly impacting white-light observation. The integration of these devices facilitated clear visualization of red fluorescence from gels containing Pp IX at concentrations of 5 µM or higher. Additionally, when observing subcutaneous human CCA tumor models in nude mice treated with 5-ALA, we successfully demonstrated distinct red fluorescence from Pp IX accumulation in tumors compared to peritumoral subcutaneous areas. CONCLUSION: The integration of our device combination holds promise for real-time 5-ALA-PDD in human CCA, potentially enhancing the diagnostic accuracy for this complex condition.

Osteoblast-specific down-regulation of NLRP3 inflammasome by aptamer-functionalized liposome nanoparticles improves bone quality in postmenopausal osteoporosis rats.

Rationale: NLRP3 inflammasome is critical in the development and progression of many metabolic diseases driven by chronic inflammation, but its effect on the pathology of postmenopausal osteoporosis (PMOP) remains poorly understood. Methods: We here firstly examined the levels of NLRP3 inflammasome in PMOP patients by ELISA. Then we investigated the possible mechanisms underlying the effect of NLRP3 inflammasome on PMOP by RNA sequencing of osteoblasts treated with NLRP3 siRNA and qPCR. Lastly, we accessed the effect of decreased NLRP3 levels on ovariectomized (OVX) rats. To specifically deliver NLRP3 siRNA to osteoblasts, we constructed NLRP3 siRNA wrapping osteoblast-specific aptamer (CH6)-functionalized lipid nanoparticles (termed as CH6-LNPs-siNLRP3). Results: We found that the levels of NLRP3 inflammasome were significantly increased in patients with PMOP, and were negatively correlated with estradiol levels. NLRP3 knock-down influenced signal pathways including immune system process, interferon signal pathway. Notably, of the top ten up-regulated genes in NLRP3-reduced osteoblasts, nine genes (except Mx2) were enriched in immune system process, and five genes were related to interferon signal pathway. The in vitro results showed that CH6-LNPs-siNLRP3 was relatively uniform with a dimeter of 96.64 ± 16.83 nm and zeta potential of 38.37 ± 1.86 mV. CH6-LNPs-siNLRP3 did not show obvious cytotoxicity and selectively delivered siRNA to bone tissue. Moreover, CH6-LNPs-siNLRP3 stimulated osteoblast differentiation by activating ALP and enhancing osteoblast matrix mineralization. When administrated to OVX rats, CH6-LNPs-siNLRP3 promoted bone formation and bone mass, improved bone microarchitecture and mechanical properties by decreasing the levels of NLRP3, IL-1ß and IL-18 and increasing the levels of OCN and Runx2. Conclusion: NLRP3 inflammasome may be a new biomarker for PMOP diagnosis and plays a key role in the pathology of PMOP. CH6-LNPs-siNLRP3 has potential application for the treatment of PMOP.

Gut microbiota-based machine-learning signature for the diagnosis of alcohol-associated and metabolic dysfunction-associated steatotic liver disease.

Alcoholic-associated liver disease (ALD) and metabolic dysfunction-associated steatotic liver disease (MASLD) show a high prevalence rate worldwide. As gut microbiota represents current state of ALD and MASLD via gut-liver axis, typical characteristics of gut microbiota can be used as a potential diagnostic marker in ALD and MASLD. Machine learning (ML) algorithms improve diagnostic performance in various diseases. Using gut microbiota-based ML algorithms, we evaluated the diagnostic index for ALD and MASLD. Fecal 16S rRNA sequencing data of 263 ALD (control, elevated liver enzyme [ELE], cirrhosis, and hepatocellular carcinoma [HCC]) and 201 MASLD (control and ELE) subjects were collected. For external validation, 126 ALD and 84 MASLD subjects were recruited. Four supervised ML algorithms (support vector machine, random forest, multilevel perceptron, and convolutional neural network) were used for classification with 20, 40, 60, and 80 features, in which three nonsupervised ML algorithms (independent component analysis, principal component analysis, linear discriminant analysis, and random projection) were used for feature reduction. A total of 52 combinations of ML algorithms for each pair of subgroups were performed with 60 hyperparameter variations and Stratified ShuffleSplit tenfold cross validation. The ML models of the convolutional neural network combined with principal component analysis achieved areas under the receiver operating characteristic curve (AUCs) > 0.90. In ALD, the diagnostic AUC values of the ML strategy (vs. control) were 0.94, 0.97, and 0.96 for ELE, cirrhosis, and liver cancer, respectively. The AUC value (vs. control) for MASLD (ELE) was 0.93. In the external validation, the AUC values of ALD and MASLD (vs control) were > 0.90 and 0.88, respectively. The gut microbiota-based ML strategy can be used for the diagnosis of ALD and MASLD.ClinicalTrials.gov NCT04339725.

Electrochemical biosensors in early leukemia detection.

Leukemia, a type of blood cancer marked by an abnormal increase in white blood cells, poses a significant challenge to healthcare. The key to successful treatment lies in early detection. However, traditional methods often fall short. This review investigates the potential of electrochemical biosensors for a more accurate and earlier diagnosis of leukemia. Electrochemical biosensors are compact devices that transform biological interactions into electrical signals. Their small size, ease of use, and minimal sample requirements make them perfectly suited for point-of-care applications. Their remarkable sensitivity and specificity enable the detection of subtle biomolecular changes associated with leukemia, which is crucial for early disease detection. This review delves into studies that have utilized these biosensors to identify various types of leukemia. It examines the roles of electrodes, biorecognition elements, and signal transduction mechanisms. The discussion includes the integration of nanomaterials such as gold nanoparticles and nitrogen-doped graphene into biosensor design. These materials boost sensitivity, enhance signal amplification, and facilitate multi-analyte detection, thereby providing a more holistic view of the disease. Beyond technical advancements, the review underscores the practical benefits of these biosensors. Their portability makes them a promising tool for resource-constrained settings, enabling swift diagnosis in remote areas or at a patient's bedside. The potential for monitoring treatment effectiveness and detecting minimal residual disease to prevent relapse is also explored. This review emphasizes the transformative potential of electrochemical biosensors in combating leukemia. By facilitating earlier and more accurate diagnosis, these biosensors stand to revolutionize patient care and enhance treatment outcomes.

Prenatal genetic diagnosis of fetuses with dextrocardia using whole exome sequencing in a tertiary center.

To evaluate the genetic etiology of fetal dextrocardia, associated ultrasound anomalies, and perinatal outcomes, we investigated the utility of whole exome sequencing (WES) for prenatal diagnosis of dextrocardia. Fetuses with dextrocardia were prospectively collected between January 2016 and December 2022. Trio-WES was performed on fetuses with dextrocardia, following normal karyotyping and/or chromosomal microarray analysis (CMA) results. A total of 29 fetuses with dextrocardia were collected, including 27 (93.1%) diagnosed with situs inversus totalis and 2 (6.9%) with situs inversus partialis. Cardiac malformations were present in nine cases, extra-cardiac anomalies were found in seven cases, and both cardiac and extra-cardiac malformations were identified in one case. The fetal karyotypes and CMA results of 29 cases were normal. Of the 29 cases with dextrocardia, 15 underwent WES, and the other 14 cases refused. Of the 15 cases that underwent WES, clinically relevant variants were identified in 5/15 (33.3%) cases, including the diagnostic variants DNAH5, DNAH11, LRRC56, PEX10, and ZIC3, which were verified by Sanger sequencing. Of the 10 cases with non-diagnostic results via WES, eight (80%) chose to continue the pregnancies. Of the 29 fetuses with dextrocardia, 10 were terminated during pregnancy, and 19 were live born. Fetal dextrocardia is often accompanied by cardiac and extra-cardiac anomalies, and fetal dextrocardia accompanied by situs inversus is associated with a high risk of primary ciliary dyskinesia. Trio-WES is recommended following normal karyotyping and CMA results because it can improve the diagnostic utility of genetic variants of fetal dextrocardia, accurately predict fetal prognosis, and guide perinatal management and the reproductive decisions of affected families.

The 30th Annual Prostate Cancer Foundation Scientific Retreat Report.

BACKGROUND: The 30th Annual Prostate Cancer Foundation (PCF) Scientific Retreat was held at the Omni La Costa Resort in Carlsbad, CA, from October 26 to 28, 2023. A hybrid component was included for virtual attendees. METHODS: The Annual PCF Scientific Retreat is a leading international scientific conference focused on pioneering, unpublished, and impactful studies across the spectrum of basic through clinical prostate cancer research, as well as research from related fields with significant potential for improving prostate cancer research and patient outcomes. RESULTS: The 2023 PCF Retreat concentrated on key areas of research, including: (i) the biology of cancer stem cells and prostate cancer lineage plasticity; (ii) mechanisms of treatment resistance; (iii) emerging AI applications in diagnostic medicine; (iv) analytical and computational biology approaches in cancer research; (v) the role of nerves in prostate cancer; (vi) the biology of prostate cancer bone metastases; (vii) the contribution of ancestry and genomics to prostate cancer disparities; (viii) prostate cancer 3D genomics; (ix) progress in new targets and treatments for prostate cancer; (x) the biology and translational applications of tumor extracellular vesicles; (xi) updates from PCF TACTICAL Award teams; (xii) novel platforms for small molecule molecular glues and binding inhibitors; and (xiii) diversity, equity and inclusion strategies for advancing cancer care equity. CONCLUSIONS: This meeting report summarizes the presentations and discussions from the 2023 PCF Scientific Retreat. We hope that sharing this information will deepen our understanding of current and emerging research and drive future advancements in prostate cancer patient care.