Personalized Therapy Research Articles

Challenges and perspectives in use of artificial intelligence to support treatment recommendations in clinical oncology.

Artificial intelligence (AI) promises to be the next revolutionary step in modern society. Yet, its role in all fields of industry and science need to be determined. One very promising field is represented by AI-based decision-making tools in clinical oncology leading to more comprehensive, personalized therapy approaches. In this review, the authors provide an overview on all relevant technical applications of AI in oncology, which are required to understand the future challenges and realistic perspectives for decision-making tools. In recent years, various applications of AI in medicine have been developed focusing on the analysis of radiological and pathological images. AI applications encompass large amounts of complex data supporting clinical decision-making and reducing errors by objectively quantifying all aspects of the data collected. In clinical oncology, almost all patients receive a treatment recommendation in a multidisciplinary cancer conference at the beginning and during their treatment periods. These highly complex decisions are based on a large amount of information (of the patients and of the various treatment options), which need to be analyzed and correctly classified in a short time. In this review, the authors describe the technical and medical requirements of AI to address these scientific challenges in a multidisciplinary manner. Major challenges in the use of AI in oncology and decision-making tools are data security, data representation, and explainability of AI-based outcome predictions, in particular for decision-making processes in multidisciplinary cancer conferences. Finally, limitations and potential solutions are described and compared for current and future research attempts.

INUDGE: Integration of liquid biopsy based next generation gene sequencing in newly diagnosed NSCLC—A stepped wedge cluster randomized clinical trial.

TPS8664 Background: The development of targeted therapies has changed the treatment paradigm for non–small cell lung cancer (NSCLC). With the growing number of FDA approved targeted therapies, current NCCN guidelines recommend comprehensive molecular genotyping prior to first line (1L) therapy for all newly diagnosed patients with metastatic non-squamous (mNSq) NSCLC to enable the delivery of personalized therapy. Based on prior work that found improved detection of clinically actionable alterations through concurrent plasma (P) based next-generation gene sequencing (NGS) and tissue NGS, we piloted a behavioral economics (BE) informed electronic health record (EHR)-based “nudge” intervention designed to facilitate P NGS at the new patient medical oncology visit. After implementing across 3 practices within the University of Pennsylvania Health System (UPHS), our findings demonstrated that EHR-based nudges are feasible and can promote guideline concordant diagnostic testing at both community and academic sites (1). Methods: Design: NCT05853887 is a prospective stepped wedge cluster randomized trial that includes 3 “clusters”, representing 6 community-based cancer centers. The primary objective of this trial is to test the effectiveness of a BE informed EHR nudge intervention to increase availability of comprehensive molecular test results before 1L therapy by incorporating concurrent tissue and plasma-based molecular testing. Secondary objectives include evaluation of contextual mechanisms contributing to the adoption, reach, and effectiveness of EHR nudge interventions with a lens for health equity. Intervention: An EHR-based nudge that allows for default order placement of P NGS testing at the time of the first medical oncology visit will be implemented. Subsequently, results detected on the default P NGS order will be conveyed to providers in the form of an electronic clinical decision support notification. Statistical Methods:Sample size is based on estimates of the proportion of patients undergoing comprehensive molecular testing prior to 1L. A sample of 3 clusters in a complete stepped-wedge cluster-randomized design with 4 time periods (including the baseline), 3 steps, 1 cluster(s) switching from control to treatment at each step, and an average of 120 subjects per cluster with an average of 30 subjects per cluster per time period (for a total sample size of 360 subjects) will provide 80% power to detect a difference between proportions of 0.21701. Patients with histological, or cytological diagnosis of Stage IV mNSq NSCLC who have not yet received systemic treatment for metastatic disease are eligible for inclusion. Enrollment is ongoing: Cluster 1 began enrollment in June 2023, Cluster 2 in October 2023, and Cluster 3 is planned for February 2024. 1. Aggarwal C et al, JCO, Vol 40, Num 16, Suppl Jun 2022. Clinical trial information: NCT05853887 .

Hematopoietic stem cell transplantation and cellular therapy in persons living with HIV.

Summarize the latest research of both stem cell transplantation and cellular therapy and present the implications with respect to persons with HIV (PWH), hematologic malignancies, and HIV-1 cure. Allogeneic (alloSCT) and autologous (autoSCT) stem cell transplantation have been shown to be well tolerated and effective regardless of HIV-1 status. AlloSCT leads to a decrease in the HIV-1 latently infected reservoir orders of magnitude below that achieved with antiretroviral therapy (ART) alone. Utilization of CCR5Δ2/Δ32 donors in an alloSCT has resulted in HIV-1 cures. In the last 12 months, three cases of cure have been published, giving further insight into the conditions required for HIV-1 control. Other advances in the treatment of hematological cancers include chimeric antigen receptor T-cell (CART) therapy, which are active in PWH with lymphoma. Here we discuss the advances in SCT and cellular therapy in PWH and cancer. Additionally, we discuss how these technologies are being utilized to achieve HIV-1 cure.

Illuminate: Depression diagnosis, explanation and proactive therapy using prompt engineering

Traditional methods of depression detection on social media forums can classify whether a user is depressed, but they often lack the capacity for human-like explanations and interactions. This paper proposes a next-generation paradigm for depression detection and treatment strategies. It employs three Large Language Models (LLMs) - Generative Pre-trained Transformer 4, Llama, and Gemini, each fine-tuned using specially engineered prompts to effectively diagnose, explain, and suggest therapeutic interventions for depression. These prompts are designed to guide the models in analyzing textual data from clinical interviews and online forums, ensuring nuanced and context-aware responses. The study utilizes a few-shot prompting methodology for the Diagnosis and Explanation component. This technique is optimized to provide DSM-5 based analysis and explanation, enhancing the model’s ability to identify and articulate depressive symptoms accurately. Models engage in empathetic dialogue management, guided by resources from Psychology Database and a Cognitive Behavioral Therapy guide, and fine-tuned using Chain of Thought and Tree of Thought prompting techniques. This facilitates meaningful interactions with individuals facing major depressive disorders, fostering a supportive and understanding environment. The research innovates in case conceptualization, treatment planning and therapeutic interventions by creating the Illuminate Database to guide the models in offering personalized therapy. The quantitative analysis of the study is demonstrated through metrics such as F1 scores, Precision, Recall, Cosine similarity, and ROUGE score across different test sets. This comprehensive approach offered through a mobile application prototype, with established psychological methodologies showcases the potential of LLMs in revolutionizing diagnosis and treatment strategies.

The radiomorphological appearance of the invasive margin in pancreatic cancer is associated with tumor budding

PurposePancreatic cancer (PDAC) is characterized by infiltrative, spiculated tumor growth into the surrounding non-neoplastic tissue. Clinically, its diagnosis is often established by magnetic resonance imaging (MRI). At the invasive margin, tumor buds can be detected by histology, an established marker associated with poor prognosis in different types of tumors.MethodsWe analyzed PDAC by determining the degree of tumor spiculation on T2-weighted MRI using a 3-tier grading system. The grade of spiculation was correlated with the density of tumor buds quantified in histological sections of the respective surgical specimen according to the guidelines of the International Tumor Budding Consensus Conference (n = 28 patients).Results64% of tumors revealed intermediate to high spiculation on MRI. In over 90% of cases, tumor buds were detected. We observed a significant positive rank correlation between the grade of radiological tumor spiculation and the histopathological number of tumor buds (rs = 0.745, p < 0.001). The number of tumor buds was not significantly associated with tumor stage, presence of lymph node metastases, or histopathological grading (p ≥ 0.352).ConclusionOur study identifies a readily available radiological marker for non-invasive estimation of tumor budding, as a correlate for infiltrative tumor growth. This finding could help to identify PDAC patients who might benefit from more extensive peripancreatic soft tissue resection during surgery or stratify patients for personalized therapy concepts.

IBS-es betegek egészségmagatartása, megoldáskeresése és ennek hatása az IBS- ből való gyógyulásról alkotott véleményükre

Irritable Bowel Syndrome is a much-researched functional gastrointestinal disease. The diagnosis and finding personalized therapy is a lengthy process. Our study aims to find out about the IBS patient’s search for solution: what specialists they turn to, what therapies they tried with what results, how strictly they follow specialists’ instructions, when do they start looking for a solution themselves, how much they trust the different specialists and what they think about their chances of recovery. We looked for volunteers with IBS disease on social platforms. From 45 volunteers, 27 submitted their responses to our survey. The results were evaluated with statistics done in Excel and SPSS: Pearson’s chi square, Fisher’s exact, standard deviation and distribution. Summary of results: there is a significant relationship between the onset of symptoms and the time when patients turn to a physician. With regards to the relationship between therapies applied and their efficacy on remediating symptoms, there was no significant relationship. Similarly, there was no significant relationship between strictly following the indications and their opinion about chances of recovery.

TES and SLC40A1 as Potential Biomarkers for Predicting Survival in T-Cell Acute Lymphoblastic Leukemia.

Identifying patients with high-risk T-cell acute lymphoblastic leukemia (T-ALL) is crucial for personalized therapy; however, the lack of robust biomarkers hinders prognosis assessment. To address this issue, our study aimed to screen and validate genes whose expression may serve as predictive indicators of outcomes in T-ALL patients while also investigating the underlying molecular mechanisms. Differentially expressed genes (DEGs) between T-ALL patients and healthy controls were identified by integrating data from three independent public datasets. Functional annotation of these DEGs and protein-protein interactions were also conducted. Further, we enrolled a prospective cohort of T-ALL patients (n = 20) at our center, conducting RNA-seq analysis on their bone marrow samples. Survival-based univariate Cox analysis was employed to identify gene expressions related to survival, and an intersection algorithm was sequentially applied. Furthermore, we validated the identified genes using cases from the Therapeutically Applicable Research to Generate Effective Treatments database, plotting Kaplan-Meier curves for secondary validation. Through the integration of survival-related genes with DEGs identified in T-ALL, our analysis revealed six T-ALL-specific genes, the expression levels of which were linked to prognostic value. Notably, the independent prognostic value of SLC40A1 and TES expression levels was confirmed in both an external cohort and a prospective cohort at our center. In summary, our preliminary study indicates that the expression levels of TES and SLC40A1 genes show promise as potential indicators for predicting survival outcomes in T-ALL patients.

Decreased but persistent epigenetic age acceleration is associated with changes in T-cell subsets after initiation of highly active antiretroviral therapy in persons living with HIV.

Persons living with HIV (PLWH) experience the early onset of age-related illnesses, even in the setting of successful human immunodeficiency virus (HIV) suppression with highly active antiretroviral therapy (HAART). HIV infection is associated with accelerated epigenetic aging as measured using DNA methylation (DNAm)-based estimates of biological age and of telomere length (TL). DNAm levels (Infinium MethylationEPIC BeadChip) from peripheral blood mononuclear cells from 200 PLWH and 199 HIV-seronegative (SN) participants matched on chronologic age, hepatitis C virus, and time intervals were used to calculate epigenetic age acceleration, expressed as age-adjusted acceleration residuals from 4 epigenetic clocks [Horvath's pan-tissue age acceleration residual (AAR), extrinsic epigenetic age acceleration (EEAA), phenotypic epigenetic age acceleration (PEAA), and grim epigenetic age acceleration (GEAA)] plus age-adjusted DNAm-based TL (aaDNAmTL). Epigenetic age acceleration was compared for PLWH and SN participants at two visits: up to 1.5years prior and 2-3years after HAART (or equivalent visits). Flow cytometry was performed in PLWH and SN participants at both visits to evaluate T-cell subsets. Epigenetic age acceleration in PLWH decreased after the initiation of HAART but remained greater post-HAART than that in age-matched SN participants, with differences in medians of 6.6, 9.1, and 7.7 years for AAR, EEAA, and PEAA, respectively, and 0.39 units of aaDNAmTL shortening (all p < 0.001). Cumulative HIV viral load after HAART initiation was associated with some epigenetic acceleration (EEAA, PEAA, and aaDNAmTL), but even PLWH with undetectable HIV post-HAART showed persistent epigenetic age acceleration compared to SN participants (p < 0.001). AAR, EEAA, and aaDNAmTL showed significant associations with total, naïve, and senescent CD8 T-cell counts; the total CD4 T-cell counts were associated with AAR, EEAA, and PEAA (p = 0.04 to <0.001). In an epigenome-wide analysis using weighted gene co-methylation network analyses, 11 modules demonstrated significant DNAm differences pre- to post-HAART initiation. Of these, nine were previously identified as significantly different from pre- to post-HIV infection but in the opposite direction. In this large longitudinal study, we demonstrated that, although the magnitude of the difference decreases with HAART is associated with the cumulative viral load, PLWH are persistently epigenetically older than age-matched SN participants even after the successful initiation of HAART, and these changes are associated with changes in T-cell subsets.

CARTAR: a comprehensive web tool for identifying potential targets in chimeric antigen receptor therapies using TCGA and GTEx data.

Chimeric antigen receptor (CAR) therapy has emerged as a ground-breaking advancement in cancer treatment, harnessing the power of engineered human immune cells to target and eliminate cancer cells. The escalating interest and investment in CAR therapy in recent years emphasize its profound significance in clinical research, positioning it as a rapidly expanding frontier in the field of personalized cancer therapies. A crucial step in CAR therapy design is choosing the right target as it determines the therapy's effectiveness, safety and specificity against cancer cells, while sparing healthy tissues. Herein, we propose a suite of tools for the identification and analysis of potential CAR targets leveraging expression data from The Cancer Genome Atlas and Genotype-Tissue Expression Project, which are implemented in CARTAR website. These tools focus on pinpointing tumor-associated antigens, ensuring target selectivity and assessing specificity to avoid off-tumor toxicities and can be used to rationally designing dual CARs. In addition, candidate target expression can be explored in cancer cell lines using the expression data for the Cancer Cell Line Encyclopedia. To our best knowledge, CARTAR is the first website dedicated to the systematic search of suitable candidate targets for CAR therapy. CARTAR is publicly accessible at https://gmxenomica.github.io/CARTAR/.

#2423 Molecular classification of lupus nephritis based on immune pathway profiling—correlations with histological class and clinical manifestations

Abstract Background and Aims The ISN/RPS 2003 classification is the current gold standard histopathological classification for lupus nephritis (LN), and has important roles in guiding treatment and predicting prognosis. The classification system, largely based on light microscopy features, does not inform the underlying immunological aberrations. A classification based on molecular typing of immune-related pathways may better stratify LN patients and help select treatments that target specific immune cascades. Method The normalized expression matrix and clinical data were extracted from Gene Expression Omnibus database, Human Cell Atlas (Hacohen-Human-CELseq2) and Nephroseq. Four chip datasets (GSE127797, 32591, 69438 and 37463) and one single cell dataset were analyzed. Sample gene set enrichment analysis (ssGSEA) method was used to derive immune phenotype with gene expression data. The hierarchical clustering with ssGSEA scores of selected gene sets was used to classify LN tissues. Functional characterization and protein-protein interactions of the differential expression genes (DEGs) of three groups were compared with healthy donors using Metascape (absolute log FC &amp;gt; 1 and adjusted P-value &amp;lt; .05). Kruskal-Walli test and chi-square test were performed to compare the clinical parameters of the different groups. Results We analyzed 96 immune-related gene sets in the glomerular and tubulointerstitial regions of LN kidney biopsy samples. The immune-related genes were categorized into three clusters (Immune-Mild, Immune-Moderate and Immune-Severe) based on the degree of immune pathway activation. Interferon-stimulated genes are a hallmark of LN, with high signals in all three clusters. When compared to other groups, the Immune-Mild cluster from the glomerular region showed no T-cell activation or T cell receptor pathway. Also, the Immune-Mild (tubulointerstitial) group also showed minimal lymphocyte activation or migration. Protein-protein interactions networks with MOCDE components further suggested that STAT1 and ISG15 the hub genes in different clusters. The gene expressions of hub genes were then compared among epithelial cells and 21 immune cells from LN and heath controls. Immune clusters are correlated with clinical phenotypes, in which Immune-Mild Group (Glomerular) showed high prevalence of Class II and V LN, and Immune-Mild (Tubulointerstitial) showed more Class V LN. Immune-Severe group also showed higher degree of proteinuria and blood pressure but lower eGFR compared to Immune-Moderate and Immune-Mild groups. Conclusion A new molecular classification of LN based on the degree of immune pathway activation shows correlation with clinical manifestations and may potentially guide personalized therapy in LN patients.

Early Breast Cancer Risk Assessment: Integrating Histopathology with Artificial Intelligence.

Effective risk assessment in early breast cancer is essential for informed clinical decision-making, yet consensus on defining risk categories remains challenging. This paper explores evolving approaches in risk stratification, encompassing histopathological, immunohistochemical, and molecular biomarkers alongside cutting-edge artificial intelligence (AI) techniques. Leveraging machine learning, deep learning, and convolutional neural networks, AI is reshaping predictive algorithms for recurrence risk, thereby revolutionizing diagnostic accuracy and treatment planning. Beyond detection, AI applications extend to histological subtyping, grading, lymph node assessment, and molecular feature identification, fostering personalized therapy decisions. With rising cancer rates, it is crucial to implement AI to accelerate breakthroughs in clinical practice, benefiting both patients and healthcare providers. However, it is important to recognize that while AI offers powerful automation and analysis tools, it lacks the nuanced understanding, clinical context, and ethical considerations inherent to human pathologists in patient care. Hence, the successful integration of AI into clinical practice demands collaborative efforts between medical experts and computational pathologists to optimize patient outcomes.

The use of personal therapy by psychiatrists and psychiatry trainees: a systematic review.

It is widely acknowledged that personal therapy positively contributes to the continued personal well-being and ongoing professional development of mental health professionals, including psychiatrists. As a result, most training bodies continue to recommend personal therapy to their trainees. Given its reported value and benefits, one might hypothesize that a high proportion of psychiatrists avail of personal therapy. This systematic review seeks to investigate whether this is the case. To identify and evaluate the findings derived from all available survey-based studies reporting quantitative data regarding psychiatrists' and psychiatry trainees' engagement in personal therapy. A systematic search for survey-based studies about the use of personal therapy by psychiatric practitioners was conducted in four databases and platforms (PubMed, Scopus, Embase and EbscoHost) from inception to May 2022 following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Studies were assessed for quality using the quality assessment checklist for survey studies in psychology (Q-SSP) and findings summarized using narrative synthesis. The proportion of trainees who engaged in personal therapy ranged from a low of 13.4% in a recent UK based study to a high of 65.3% among Israeli residents. The proportion of fully qualified psychiatrists who engaged in personal therapy varied from 32.1% in South Korea to 89% in New Zealand. This review represents the first known attempt to collect and synthesize data aimed at providing insights into the past and current trends in psychiatrists' use of personal therapy across different geographic regions and career stages.

An implementation of personalized therapy in Clinical Decision Support System using adaptive transformer and hybrid deep learning network

ABSTRACT This paper suggests a new mechanism from deep learning concept for personalised therapy in Clinical Decision Support Systems (CDSS). Basically, the texts used for the observation are acquired from the standard data sources and then forwarded to the text preprocessing task. In the pre-processing phase, the punctuation and special character removal, stop word removal, and stemming process are applied to remove noise and help to eliminate the redundant information in order to improve the data quality. Further, the pre-processed text is applied to the Adaptive Transformer Net (ATN) for the feature extraction purpose, where the attributes in this task are optimally determined with the aid of the Adaptive Walrus Optimization Algorithm (AWOA). Finally, the resultant text is subjected to the Hybrid Deep Learning Network (HDLNet). The HDLNet model is implemented by integrating the ‘Residual Long Short-Term Memory (Residual LSTM) with Dilated Recurrent Neural Network (Dilated RNN)’. From the results, the sensitivity analysis performed in the implemented technique secured 3.7% more efficient than LSTM, 7.76% improved than MobileNet, 6.7% superior to residual LSTM, and 0.90% effective than dilated RNN in dataset 1. Throughout the validation, the conventional techniques are evaluated with the suggested personalised therapy in CDSS to prove its efficacy.

U-shaped relationship found between fibrinogen-to-albumin ratio and systemic inflammation response index in osteoporotic fracture patients

The relationship between the Systemic Inflammatory Response Index (SIRI) and the Fibrinogen-to-albumin ratio (FAR) has not been extensively investigated. The objective of this study was to determine the independent relationship between FAR and SIRI in people with osteoporotic fractures (OPF). A cross-sectional study was conducted using retrospective data from 3431 hospitalized OPF patients. The exposure variable in this study was the baseline FAR, while the outcome variable was the SIRI. Covariates, including age, gender, BMI, and other clinical and laboratory factors, were adjusted. Cross-correlation analysis and linear regression models were applied. The generalized additive model (GAM) investigated non-linear relationships. Adjusted analysis revealed an independent negative association between FAR and SIRI in OPF patients (β = − 0.114, p = 0.00064, 95% CI − 0.180, − 0.049). A substantial U-shaped association between FAR and SIRI was shown using GAM analysis (p < 0.001). FAR and SIRI indicated a negative association for FAR below 6.344% and a positive correlation for FAR over 6.344%. The results of our study revealed a U-shaped relationship between SIRI and FAR. The lowest conceivable FAR for a bone-loose inflammatory disease might be 6.344%, suggesting that this has particular significance for the medical diagnosis and therapy of persons with OPF. Consequently, the term "inflammatory trough" is proposed. These results offer fresh perspectives on controlling inflammation in individuals with OPF and preventing inflammatory osteoporosis.

Multimodal data integration for enhanced longitudinal prediction for cardiac and cerebrovascular events following initial diagnosis of obstructive sleep apnea syndrome.

Obstructive sleep apnea syndrome (OSAS), a prevalent condition, often coexists with intricate metabolic issues and is frequently associated with negative cardiovascular outcomes. We developed a longitudinal prediction model integrating multimodal data for cardiovascular risk stratification of patients with an initial diagnosis of OSAS. We reviewed the data of patients with new-onset OSAS who underwent diagnostic polysomnography between 2018-19. Patients were treated using standard treatment regimens according to clinical practice guidelines. Over a median follow-up of 32 months, 98/729 participants (13.4%) experienced our composite outcome. At a ratio of 7:3, cases were randomly divided into development (n = 510) and validation (n = 219) cohorts. A prediction nomogram was created using six clinical factors - sex, age, diabetes mellitus, history of coronary artery disease, triglyceride-glucose index, and apnea-hypopnea index. The prediction nomogram showed excellent discriminatory power, based on Harrell's C-index values of 0.826 (95% confidence interval (CI) = 0.779-0.873) for the development cohort and 0.877 (95% CI = 0.824-0.93) for the validation cohort. Moreover, comparing the predicted and observed major adverse cardiac and cerebrovascular events in both development and validation cohorts indicated that the prediction nomogram was well-calibrated. Decision curve analysis demonstrated the good clinical applicability of the prediction nomogram. Our findings demonstrated the construction of an innovative visualisation tool that utilises various types of data to predict poor outcomes in Chinese patients diagnosed with OSAS, providing accurate and personalised therapy. Chinese Clinical Trial Registry ChiCTR2300075727.

Development of nanobiosensor for therapeutic drug monitoring in personalized cancer treatment approach

Docetaxel is one of the most effective and safe chemotherapy drugs according to the World Health Organization, but its clinical use has been discontinued due to its various side effects. To reduce these side effects, the amount of docetaxel drug should be kept at the most effective level, it should be monitored in body fluids. Due to the limitations of traditional analytical methods used for this purpose, such as expensive and low sensitivity, labor-intensive and time-consuming complex preliminary preparation, efficient methods are required for the determination of the docetaxel level in the body. The increasing demand for the development of personalized therapy has recently spurred significant research into biosensors for the detection of drugs and other chemical compounds. In this study, an electrochemical-based portable nanobiosensor system was developed for the rapid, low-cost, and sensitive determination of docetaxel. In this context, mg-p(HEMA)-IMEO nanoparticles to be used as nanobiosensor bioactive layer was synthesized, characterized, and docetaxel determination conditions were optimized. According to the results obtained, the developed nanobiosensor system can detect docetaxel with a sensitivity of 2.22 mg/mL in a wide calibration range of 0.25-10 mg/mL, in only 15 min, in mixed media such as commercially available artificial blood serum and urine. determined. We concluded that the developed nanobiosensor system can be successfully used in routine drug monitoring as a low-cost biomedical device capable of direct, rapid, and specific drug determination within the scope of personalized treatment, providing point-of-care testing.

Transforming Screening, Risk Stratification, and Treatment Optimization in Chronic Liver Disease Through Data Science and translational Innovation

Background: Chronic liver diseases continue to face challenges in prognosis, treatment selection, disease mechanisms, screening, and therapeutic optimization. Promising innovations could address these gaps through data integration and novel analytic approaches.Main Body: MAPS-CRAFITY integrating clinical variables, AFP, and CT/MRI findings, and transformer modeling of RFA data improve HCC outcome prediction to guide management. Analyses revealing IL21R as a PBC susceptibility gene and implicating dysfunctional VWF processing in portal hypertension deliver mechanistic insights. Quantifying childhood MAFLD informs screening needs, while supporting use of G6PD deficient liver donors enables transplantation access expansion through risk stratification. Updating Baveno criteria enhances PBC prognosis, and an HCC prognostic score identifies optimal RFA candidates to maximize treatment efficacy.Conclusion: Recent research leverages diverse data types, genetics, imaging, and machine learning to develop integrated predictive systems that allow more personalized therapy selection. Elucidating molecular pathways provides therapeutic targets and prognostic biomarkers. Evidence-based screening and risk models facilitate delivering tailored interventions. Optimization of current modalities through prognostic validation and patient selection improves real-world effectiveness. Multifaceted modern research approaches promise to address unmet needs and transform hepatology care.

From rare events to systematic data collection: the RESCUED registry for sudden cardiac death in the young in Germany.

Approximately one-third of sudden cardiac deaths in the young (SCDY) occur due to a structural cardiac disease. Forty to fifty percent of SCDY cases remain unexplained after autopsy (including microscopic and forensic-toxicological analyses), suggesting arrhythmia syndromes as a possible cause of death. Due to the possible inheritability of these diseases, blood relatives of the deceased may equally be carriers of the causative genetic variations and therefore may have an increased cardiac risk profile. A better understanding of the forensic, clinical, and genetic data might help identify a subset of the general population that is at increased risk of sudden cardiac death. The German registry RESCUED (REgistry for Sudden Cardiac and UnExpected Death) comprises information about SCDY fatalities and clinical and genetic data of both the deceased and their biological relatives. The datasets collected in the RESCUED registry will allow for the identification of leading causes of SCDY in Germany and offer unique possibilities of scientific analyses with the aim of detecting unrecognized trends, risk factors, and clinical warning signs of SCDY. In a pilot phase of 24months, approximately 180 SCDY cases (< 50years of age) and 500 family members and clinical patients will be included. RESCUED is the first registry in Germany collecting comprehensive data of SCDY cases and clinical data of the biological relatives reviewed by cardiac experts. RESCUED aims to improve individual risk assessment and public health approaches by directing resources towards early diagnosis and evidence-based, personalized therapy and prevention in affected families. Trial registration number (TRN): DRKS00033543.

Thermoluminescence dosimetry (TLD) in a 3 T magnetic resonance imaging (MRI) environment: implications for personnel exposure monitoring.

Thermoluminescent dosimeters (TLDs) serve as compact and user-friendly tools for various applications, including personal radiation dosimetry and radiation therapy. This study explores the potential of utilizing TLD-100 personal dosimetry, conventionally applied in PET/CT (positron emission tomography/computed tomography) settings, in the PET/MRI (magnetic resonance imaging) environment. The integration of MRI into conventional radiotherapy and PET systems necessitates ionizing radiation dosimetry in the presence of static magnetic fields. In this study, TLD-100 dosimeters were exposed on the surface of a water-filled cylindrical phantom containing PET-radioisotope and positioned on the patient table of a 3 T PET/MRI, where the magnetic field strength is around 0.2 T, aiming to replicate real-world scenarios experienced by personnel in PET/MRI environments. Results indicate that the modified MR-safe TLD-100 personal dosimeters exhibit no significant impact from the static magnetic field of the 3 T PET/MRI, supporting their suitability for personal dosimetry in PET/MRI settings. This study addresses a notable gap in existing literature on the effect of MRI static magnetic field on TLDs.

Versatile Biomass‐Based Injectable Photothermal Hydrogel for Integrated Regenerative Wound Healing and Skin Bioelectronics

AbstractThe continuously growing utilization of wound healing materials and skin bioelectronics urges the development of flexible hydrogels for personal therapy and health management. Versatile conductive hydrogels prepared from natural biomass are ideal candidates as one of the promising solutions for chronic wound management. Here, the study proposes a kind of robust (strain: 1560.8%), adhesive, self‐healing, injectable, antibacterial (sterilization rate: 99%), near‐infrared (NIR) photothermal responsive, biocompatible, and conductive hydrogel (CPPFe@TA) composed of carboxymethyl cellulose and tannic acid/iron ion complex (TA@Fe3+), featuring rapid self‐assembly and tunable crosslinking time. TA@Fe3+ facilitated the self‐catalysis of the polymerization reaction, and the crosslinking time could be controlled by adjusting Fe3+ concentration. Under NIR irradiation, the hydrogel exhibited remarkable photothermal antibacterial performance. In the full‐thickness skin defect repair experiment on mice, the prepared hydrogel dressing significantly enhanced wound healing. After 14 days, the wound healing rate (95.49%) of CPPFe@TA3 hydrogel + NIR treatment greatly exceeded that of commercial dressings. Meanwhile, the hydrogels has good electrical conductivity and thermo‐responsiveness, making them promising for skin bioelectronics in physiological signal monitoring and rehabilitation exercise management. This work therefore offers a promising strategy for developing versatile biomass‐based hydrogels, which is expected to be applicable to integrated regenerative wound healing and skin bioelectronics.