Personalized Therapy Research Articles

Deep learning-based algorithm for automatic quantification of nigrosome-1 and Parkinsonism classification using susceptibility map-weighted MRI.

To develop and validate a deep learning-based automatic quantification for nigral hyperintensity and a classification algorithm for neurodegenerative parkinsonism using susceptibility map-weighted imaging (SMwI). We retrospectively collected 450 participants (210 with idiopathic Parkinson's disease [IPD] and 240 individuals in the control group) for training data between November 2022 and May 2023, and 237 participants (168 with IPD, 58 with essential tremor, and 11 with drug-induced Parkinsonism) for validation data between July 2021 and January 2022. SMwI data were reconstructed from multi-echo GRE. Diagnostic performance for diagnosing IPD was assessed using deep learning-based automatic quantification (Heuron NI) and classification (Heuron IPD) model. Reference standard for IPD was based on 18F-FP-CIT PET finding. Additionally, the correlation between the H&Y stage and volume of nigral hyperintensity in patients with IPD was assessed. Quantification of nigral hyperintensity using Heuron NI showed AUC of 0.915 (95% CI, 0.872-0.947) and 0.928 (95% CI, 0.887-0.957) on the left and right, respectively. Classification of nigral hyperintensity abnormality using Heuron IPD showed AUC of 0.967 (95% CI, 0.881-0.991) and 0.976 (95% CI, 0.948-0.992) on the left and right, respectively. H&Y score ≥ 3 showed significant smaller nigral hyperintensity volume (1.43 ± 1.19 mm3) compared to H&Y score 1-2.5 (1.98 ± 1.63 mm3; p = 0.008). Our deep learning-based model proves rapid, accurate automatic quantification of nigral hyperintensity, facilitating IPD diagnosis, symptom severity prediction, and patient stratification for personalized therapy. Further study is warranted to validate the findings across various clinical settings. IPD = Idiopathic Parkinson's disease; SN = substantia nigra; SMwI = susceptibility map weighted imaging; QSM = quantitative susceptibility mapping; CNN = convolutional neural network; ICV = intracranial volume.

Proteomic profiling of oral squamous cell carcinoma tissues reveals altered immune-related proteins: implications for personalized therapy

ABSTRACT Objectives Oral squamous cell carcinoma poses a substantial global health challenge marked by rising mortality rate. Recently, immunotherapy has shown promising results in cancer management by enhancing immune response. Thus, identifying additional immune-related markers is critical for advancing immunotherapy treatments. Methods Data-independent acquisition (DIA) mass spectrometry approach was used to explore differentially expressed immune-related proteins in oral cancer tissues compared to adjacent non-cancerous tissues. Functional significance was identified through Gene Ontology, pathway, and network analysis. Gene expression of identified proteins was validated using transcriptomic data. Results DIA analysis identified 29,459 precursors corresponding to 3429 proteins. Among these, 1060 proteins were differentially expressed, with 166 being immune-related. Differentially regulated proteins were involved in innate immune response, mitochondrial ATP synthesis, and neutrophil degranulation. Pathway analysis of immune-related proteins showed perturbation in anti-tumor immunity-related pathways such as interferon signaling, TCR signaling, PD-1 signaling, and antigen processing and presentation. Significance of these pathways was further reinforced by the strong interactions identified in the protein–protein interaction network analysis. Additionally, gene expression analysis showed similar mRNA expression patterns for key proteins involved in altered pathways, including ISG15, IFIT1/3, HLA-A/C and OAS2/3. Conclusions Further validation of these proteins could establish them as potential targets for personalized therapy.

Sex differences in overweight and obese patients undergoing high-power short-duration pulmonary vein isolation for atrial fibrillation: an observational cohort study.

Atrial fibrillation (AF) is the most common heart rhythm disorder worldwide. As treatment methods evolve, optimizing personalized therapy based on patient characteristics, such as sex, becomes crucial. This study investigates sex differences in high-power short-duration (HPSD) pulmonary vein isolation (PVI) in overweight and obese patients. We analyzed data from 189 overweight and obese patients who underwent HPSD PVI for AF, comparing demographic information, procedural details, outcomes, and complications between male and female patients. Our analysis revealed fewer women underwent PVI compared to men, with women typically older and showing more pronounced changes in the left atrial substrate. Despite these differences, the safety and efficacy of PVI were comparable between sexes, including in the BMI ≥ 30kg/m2 subgroup and after age adjustment. The findings emphasize the need for early AF screening in women to prevent treatment delays and show that considering sex-specific differences in fat distribution can improve procedural outcomes. These insights support the need for tailored management strategies for AF in overweight and obese populations, addressing sex-specific risks and anatomical variations.

OGC SO46 - Gastric Cancer: A Comprehensive Literature Review

Abstract Background Gastric cancer stands as a significant global health concern with high mortality rates urging urgent attention. This article comprehensively explores the epidemiology, anatomy, risk factors, pathophysiology, clinical presentation, diagnosis, staging, treatment modalities, prevention strategies, and survival rates associated with gastric cancer. Notably, Helicobacter pylori infection, dietary choices, and intricate stomach anatomy play pivotal roles in disease development. Early detection, utilizing staging, grading, and genetic testing for personalized treatment approaches is emphasized. Treatment modalities encompass surgery, chemotherapy, radiation therapy, targeted therapy, and immunotherapy. Prevention strategies involve lifestyle changes, and genetic counseling. Survival rates vary by stage, highlighting the need for individualized care. Method This comprehensive review was conducted by systematically analyzing a wide range of published literature on gastric cancer. The search strategy involved utilizing electronic databases such as PubMed, Google Scholar, and Scopus, with specific keywords including "gastric cancer," "stomach cancer," "epidemiology," "anatomy," "risk factors," "pathophysiology," "diagnosis," "treatment modalities," "prevention," and "survival rates." Results Risk Factors: Factors such as Helicobacter pylori infection, dietary choices, tobacco use, and genetic predispositions contribute to gastric cancer risk. Pathophysiology: Genetic alterations, chronic inflammation, and dysregulated signaling pathways drive the development of gastric cancer. Clinical Presentation: Symptoms vary & diagnosis involves a combination of methods, including endoscopy and molecular profiling. Treatment Modalities: Treatment options include surgery, chemotherapy, radiation therapy, targeted therapy, and immunotherapy. Prevention Strategies: Prevention efforts focus on minimizing risk factors through lifestyle changes, screening, and genetic counseling. Survival Rates: Survival rates vary significantly by stage, ranging from 90-95% for stage 0 to less than 5% for stage IV Conclusion The intricate anatomy of the stomach plays a crucial role in disease localization, staging, and treatment planning, with genetic mutations and inflammation central to the pathophysiology. Early detection is underscored through clinical presentation and diagnosis processes, utilizing staging, grading, and genetic testing for personalized treatment approaches, including surgery, chemotherapy, radiation therapy, targeted therapy, and immunotherapy. Prevention strategies emphasize lifestyle changes and screening, while survival rates vary by stage, underscoring the need for individualized care. In conclusion, addressing gastric cancer requires a collaborative effort to reduce its impact and enhance outcomes on a global scale.

Stemness of Cancer: A Study of Triple-negative Breast Cancer From a Neuroscience Perspective.

Stemness, giving cancer cells massive plasticity enabling them to survive in dynamic (e.g. hypoxic) environments and become resistant to treatment, especially chemotherapy, is an important property of aggressive tumours. Here, we review some essentials of cancer stemness focusing on triple-negative breast cancer (TNBC), the most aggressive form of all breast cancers. TNBC cells express a range of genes and mechanisms associated with stemness, including the fundamental four "Yamanaka factors". Most of the evidence concerns the transcription factor / oncogene c-Myc and an interesting case is the expression of the neonatal splice variant of voltage-gated sodium channel subtype Nav1.5. On the whole, measures that reduce the stemness make cancer cells less aggressive, reducing their invasive/metastatic potential and increasing/restoring their chemosensitivity. Such measures include gene silencing techniques, epigenetic therapies as well as novel approaches like optogenetics aiming to modulate the plasma membrane voltage. Indeed, simply hyperpolarizing their membrane potential can make stem cells differentiate. Finally, we give an overview of the clinical aspects and exploitation of cancer/TNBC stemness, including diagnostics and therapeutics. In particular, personalised mRNA-based therapies and mechanistically meaningful combinations are promising and the emerging discipline of 'cancer neuroscience' is providing novel insights to both fundamental issues and clinical applications.

OXPHOS mediators in acute myeloid leukemia patients: Prognostic biomarkers and therapeutic targets for personalized medicine.

Despite significant advances in comprehending its tumorigenic role, the prognostic and therapeutic potential of targeting oxidative phosphorylation (OXPHOS) in acute myeloid leukemia (AML) remain obscure. The prognostic value of ~ 200 mitochondrial/OXPHOS genes as candidate biomarkers was examined in AML patients over ~ 10 years follow-up using Kaplan-Meier and Cox regression analyses. Furthermore, the transcript levels of the assessed markers were inspected in healthy bone marrow tissues and the dependencies of AML cells on the assessed genes were examined. Elevated levels of NADH:ubiquinone oxidoreductase subunit A6 (NDUFA6), succinate dehydrogenase complex flavoprotein subunit A (SDHA), solute carrier family 25 member 12 (SLC25A12), electron transfer flavoprotein subunit beta (ETFB), carnitine palmitoyltransferase 1A (CPT1A) and glutathione peroxidase 4 (GPX4) were associated with poor overall survival of AML patients. SLC25A12, ETFB and CPT1A were overexpressed in AML compared to healthy tissues. Cytochrome B5 type A (CYB5A)high, SLC25A12high and GPX4high AML patients displayed higher levels of circulating and engrafted blasts compared to low-expressing cohorts. NPM1 and SRSF2 mutations were frequent in SDHAlow and CPT1Alow AML patients respectively. FLT3-ITD, NPM1 and IDH1 mutations were prevalent in CPT1Ahigh AML patients. FLT3-ITD AMLs were more dependent on OXPHOS. This study identifies NDUFA6 and SDHA as novel companion prognostic biomarkers which might present a rational strategy for personalized therapy of AML patients.

From Agency to Responsibility: Gestalt Therapy Trainees’ Experience of Engaging in Personal Therapy

Personal therapy is a common mandatory element of counseling and psychotherapy training in the United Kingdom. Previous research has established that this practice provides trainees with a unique learning space but also is associated with financial and emotional costs. Furthermore, some argue that the practice of personal therapy is ethically questionable. At the heart of many relational modalities, such as Gestalt psychotherapy, lies the relationship between client and therapist and more research is needed to explore how trainees perceive established training methods. The current study recruited 11 trainee Gestalt practitioners and conducted one-to-one semi-structured interviews to explore their experiences of engaging in personal therapy. A number of themes emerged in reflexive thematic analysis: trainees perceived personal therapy to be associated with professional and personal gains as well as financial and emotional costs; they also viewed personal therapy to be an essential component of their training but would like to be involved in discussions around how it is shaped. We recommend that training providers and professional bodies offer such a dialogic space, and that future research and practice further considers the barriers of minority clients and trainees in particular.

Real-life data of hepatitis C treatment with direct acting antiviral therapy in persons injecting drugs or on opioid substitution therapy.

HCV treatment has been revolutionized by introduction of direct-acting antiviral therapy (DAA). Short treatment duration of eight to twelve weeks combined with significantly improved tolerability opened the opportunity to reach out to difficult-to-treat populations. Here, we retrospectively analyzed real life data on HCV treatment adherence and outcome in people who inject drugs (PWID) or on opioid substitution therapy (OST). All PWID or on OST receiving DAA therapy between 3/2021-11/2022 at an infectious disease clinic in Bonn were retrospectively analyzed. Patients received either 8weeks glecaprevir/pibrentasvir or 12weeks sofosbuvir/velpatasvir (+ ribavirin in genotype 3 cirrhotic patients). Sustained virological response (SVR) was measured 4 and 12weeks after HCV therapy. In our cohort 47 patients (68%) received treatment with glecaprevir/pibrentasvir and 22 patients (32%) sofosbuvir/velpatasvir. All 47 (100%) patients started on glecaprevir/pibrentasvir received prescriptions for the full length of therapy, while patients on sofosbuvir/velpatasvir completed 12weeks therapy in 86% and 8weeks in 14% (p = 0.029). Of 69 patients 74% were found to achieve SVR. In 20% no information is available as they were lost to follow-up. Re-infection was documented in 3 patients and one relapse in a gt3 patient with cirrhosis. High adherence and response rates to HCV treatment were found following DAA based therapy in PWID supporting the call to include difficult-to-treat populations into HCV treatment efforts on the way to HCV elimination. Treatment of OST and HCV at one institution supporting patients by a multidisciplinary team may further facilitate adherence to follow up visits enabling documentation of treatment outcomes more easily.

Advancements in the treatment of autoimmune diseases: Integrating artificial intelligence for personalized medicine

The incorporation of artificial intelligence (AI) into medical practice has considerably improved the treatment of autoimmune disorders, opening new avenues for personalized therapy. This study examines advances in AI-driven therapeutic options for autoimmune illnesses, including both current and developing treatments. Traditional therapies for autoimmune illnesses, such as immunosuppressive therapy and biologics, attempt to alleviate symptoms but frequently fall short of offering personalized care. Emerging approaches, such as precision medicine and artificial intelligence, are altering the landscape by harnessing massive volumes of patient data to better customize therapies. AI holds the ability to transform autoimmune disease therapy by enhancing diagnosis, discovering biomarkers, optimizing drug development, and personalized treatment procedures. Real-world applications and case studies are examined to demonstrate how machine learning algorithms have improved treatment tactics for rheumatoid arthritis, systemic lupus erythematosus, and multiple sclerosis. While AI has many advantages, like enhanced diagnosis accuracy and personalized therapy, it also has drawbacks, such as data privacy, the requirement for vast datasets, algorithmic bias, and a lack of explain ability. This study emphasizes the advantages of AI, such as improved patient stratification and predictive modelling, while also discussing its drawbacks, such as ethical problems and the possibility of data exploitation. AI presents intriguing prospects for treating autoimmune diseases, but more research and cooperation are required to overcome current difficulties and completely integrate AI into clinical practice.

TMET-02. CELL-INTRINSIC METABOLIC PHENOTYPES IDENTIFIED IN GLIOBLASTOMA PATIENTS USING MASS SPECTROMETRY IMAGING OF 13C-LABELED GLUCOSE METABOLISM

Abstract Glioblastoma (GB) is an inherently heterogeneous and invasive primary brain cancer. Genomic and transcriptomic studies have attempted to classify GB into subtypes that predict survival and that have different therapeutic vulnerabilities. An outstanding question and technical challenge is to visualize the metabolism of a tumour within its native microenvironment and understand to what extent its metabolism is driven by the microenvironment. Patients with GB tumours were infused with [U-13C]glucose and intra-operative multi-regional tumour samples were rapidly snap frozen for analysis using mass spectrometry imaging (MSI). Tissue microenvironment was assessed using multiplexed antibodies in immunocytochemistry (IMC) of contiguous sections. Isotope tracking in human-derived neurospheres and patient-derived xenografts were used to confirm the presence of metabolic phenotypes detected in the human samples and to test the susceptibility of each phenotype to a panel of drugs targeting cellular metabolism. Three metabolic signatures were identified in patient tumours: glycolytic, oxidative and a mixed glycolytic/oxidative phenotype. The phenotypes did not correlate with microenvironmental features, including proliferation rate, immune cell infiltration, and vascularisation. The phenotypes were retained when patient-derived cells were grown in vitro, or as orthotopically implanted xenografts, and were robust to changes in oxygen concentration, demonstrating their cell intrinsic nature. The spatial extent of the regions occupied by cells displaying these distinct metabolic phenotypes are large enough to be detected using clinically applicable metabolic imaging techniques. The phenotypes also demonstrate differential drug sensitivities, suggesting imaging of these phenotypes may be used to stratify patients for personalised therapy trials. This is the first report to demonstrate high resolution imaging of metabolic fluxes in a human tumour in vivo and the presence of different metabolic phenotypes within GB that are tumour cell intrinsic and largely independent of the tumour microenvironment.

DDDR-05. SPATIALLY RESOLVED DRUG SENSITIVITY PROFILING IN GLIOBLASTOMA

Abstract BACKGROUND Glioblastoma is the most common malignant primary brain tumor in adults, characterized by poor overall survival and an urgent need for more effective treatment strategies. Macroscopic heterogeneity, as observed through MR imaging, divides the tumor into distinct regions: a tumor-cell dense contrast-enhancing tumor core and a non-contrast-enhancing infiltration zone that appears hyperintense on T2/FLAIR imaging. The infiltration zone contains both infiltrating tumor cells and non-malignant cells from the local brain microenvironment. Although genomic and transcriptomic differences between the tumor core and the infiltration zone have been reported, functional differences, such as differential drug sensitivities, remain largely unknown. METHODS To investigate this, we collected region-specific glioblastoma patient tissue samples from both the contrast-enhancing tumor core and the non-contrast-enhancing but 5-aminolevulinic acid (5-ALA) fluorescent infiltration zone during surgery. Using pharmacoscopy, a microscopy-based single-cell drug screening platform, we assessed drug responses across regions and patients. RESULTS To determine whether drug responses within a single tumor region were more similar compared to responses across different regions we collected multiple tissue samples from each tumor region in seven patients. We identified region-specific drug responses for temozolomide and lomustine, among other drugs, in some of the patients. However, drug sensitivities were not consistent within the same region across different patients. Thus, across patients, the region-specific drug responses were obscured by patient heterogeneity. To address intra- and inter-tumor drug response heterogeneity, we developed a complementarity score based on region-specific drug response data of 60 drugs across 23 paired samples. The complementarity score identified promising drug combinations that demonstrated strong anti-tumor activity across tumor regions and patients. CONCLUSIONS Monotherapy approaches have largely failed in glioblastoma, despite extensive molecular profiling efforts aimed at identifying tumor-specific vulnerabilities. Identifying drug combinations that synergistically target the locoregional heterogeneity of glioblastoma may open up new avenues of personalized therapy.

NIMG-01. RADIOMIC DATA ANALYSIS WITH ENSEMBLE MACHINE LEARNING TECHNIQUES PREDICT GRADE, HISTOPATHOLOGIC SUBTYPE, AND 1P/19Q CO-DELETION STATUS IN PATIENTS WITH LOW-GRADE GLIOMAS

Abstract Low-grade gliomas (LGGs) are a diverse group of primary brain tumors characterized by their variable prognosis and treatment response. Traditionally, the management of LGGs has relied on histopathological analysis, which sometimes offers limited insight into tumor behavior and treatment outcomes. Recent advances in radiomics have enabled the extraction of detailed quantitative features from routine imaging, providing a non-invasive method to assess tumor characteristics. These radiomic features can capture underlying tumor heterogeneity more comprehensively than conventional methods. Integrating radiomic data with ensemble machine learning techniques offers a promising approach to enhance the prediction of tumor grade, histopathologic subtype, and genetic alterations such as 1p/19q co-deletion status in LGGs, potentially leading to more tailored therapeutic strategies. We combined radiomic-genomic data from 159 magnetic resonance imaging (MRI) scans from the updated LGG-1p19qDeletion dataset (Erickson 2020) and 114 MRI scans from the ReMIND dataset (Juvekar 2024). Expert radiologists performed segmentations. A total of 2446 extracted radiomic features were initially extracted. Feature selection was performed using wrapper- and filter-based techniques with cross-validation. Prediction models were built using machine learning techniques, including random forest and extreme gradient boosting. Using Random Forest, we achieved high accuracy in predicting histopathologic subtype (88.14%, AUC 96.68%) with precision of 87.86% and Dice score of 87.88%. For grade prediction (accuracy 76.19%, AUC 91.84%), precision was 73.91%, and the Dice score was 77.27%. For 1p/19q co-deletion status (accuracy 75.61%, AUC 82.74%), precision was 77.78%, and the Dice score was 73.68%. Our study establishes a robust radiomic workflow to predict tumor grade, histopathologic subtype, and 1p/19q co-deletion status in low-grade gliomas, enhancing diagnostic accuracy and treatment personalization. This methodology can be applied clinically to improve the stratification of treatment plans for low-grade gliomas, potentially leading to effective, personalized therapy options. Using multicentric data potentially improved external validity, something which past research lacked. Future research should focus on validating and deploying this model using observational multicentric study designs.

Adeno-Associated Viral Vectors in the Treatment of Epilepsy

Epilepsy is a brain disorder characterized by a persistent predisposition to epileptic seizures. With various etiologies of epilepsy, a significant proportion of patients develop pharmacoresistance to antiepileptic drugs, which necessitates the search for new therapeutic methods, in particular, using gene therapy. This review discusses the use of adeno-associated viral (AAV) vectors in gene therapy for epilepsy, emphasizing their advantages, such as high efficiency of neuronal tissue transduction and low immunogenicity/cytotoxicity. AAV vectors provide the possibility of personalized therapy due to the diversity of serotypes and genomic constructs, which allows for increasing the specificity and effectiveness of treatment. Promising orientations include the modulation of the expression of neuropeptides, ion channels, transcription, and neurotrophic factors, as well as the use of antisense oligonucleotides to regulate seizure activity, which can reduce the severity of epileptic disorders. This review summarizes the current advances in the use of AAV vectors for the treatment of epilepsy of various etiologies, demonstrating the significant potential of AAV vectors for the development of personalized and more effective approaches to reducing seizure activity and improving patient prognosis.

RADT-12. DERIVING IMAGING BIOMARKERS FOR PRIMARY CENTRAL NERVOUS SYSTEM LYMPHOMA USING DEEP LEARNING

Abstract PURPOSE Primary central nervous system lymphoma (PCNSL) is typically treated with chemotherapy, steroids, and/or whole brain radiotherapy (WBRT). Determining which patients benefit from WBRT after chemotherapy and which are adequately treated with chemotherapy alone remains a clinical challenge. Although WBRT is associated with improved outcomes, it also carries a risk of neurocognitive side effects. This study aims to refine PCNSL patient phenotyping by leveraging deep learning (DL) extracted imaging biomarkers to enable personalized therapy. METHODS Our study included 71 patients treated between 2009-2021. The primary outcome of interest was overall survival (OS) assessed at one- and two-year cutoffs. The DL model leveraged an 8-layer 2D convolutional neural network analyzing individual slices of post-contrast T1-weighted pre-treatment MRI scans. Survival predictions utilized a weighted voting system related to tumor size. Model performance was assessed with accuracy, sensitivity, specificity, and F1 scores. Time-dependent AUCs were calculated and C-statistics were computed to summarize the results. Kaplan-Meier (KM) survival analysis assessed differences between low and high-risk groups and were evaluated using the log-rank test. RESULTS The cohort’s average age was 65.6 years with average OS of 2.80 years. For one-year OS, the model achieved an AUC of 0.73 (0.60-0.85), accuracy of 0.73 (0.61-0.82), sensitivity of 0.72 (0.54–0.85), and specificity of 0.73 (0.58–0.84). For two-year OS, the model achieved an AUC of 0.70 (0.58-0.82), accuracy of 0.70 (0.58-0.79), sensitivity of 0.68 (0.55–0.81), and specificity of 0.71 (0.55–0.84). Model performance is presented with respective 95% confidence intervals. KM curves showed the one and two-year models effectively discriminated between low- and high-risk group, reporting c-statistics of 0.73 (p<.001) and 0.71 (p<.001) respectively. A sub-analysis confirmed consistent model performance across different tumor volumes and focality. CONCLUSION DL classifiers of PCNSL MRIs can stratify patient phenotypes beyond traditional risk paradigms. Given dissensus surrounding PCNSL treatment, DL can augment risk stratification and treatment personalization, especially with regards to WBRT decision making.

Modeling Drug Responses and Evolutionary Dynamics using Patient-Derived Xenografts Reveals Precision Medicine Strategies for Triple Negative Breast Cancer.

The inter- and intra-tumor heterogeneity of triple negative breast cancers (TNBC), which is reflected in diverse drug responses, interplays with tumor evolution. Here, we developed a preclinical experimental and analytical framework using treatment-naive TNBC patient-derived tumor xenografts (PDTX) to test their predictive value in personalized cancer treatment approaches. Patients and their matched PDTX exhibited concordant drug responses to neoadjuvant therapy using two trial designs and dosing schedules. This platform enabled analysis of non-genetic mechanisms involved in relapse dynamics. Treatment resulted in permanent phenotypic changes with functional and therapeutic consequences. High throughput drug screening methods in ex vivo patient derived tumor xenograft cells (PDTCs) revealed patient-specific drug response changes dependent on first-line therapy. This was validated in vivo, as exemplified by a change in olaparib sensitivity in tumors previously treated with clinically relevant cycles of standard-of-care chemotherapy. In summary, PDTXs provide a robust tool to test patient drug responses and therapeutic regimens and to model evolutionary trajectories. However, high inter-model variability and permanent non-genomic transcriptional changes constrain their use for personalized cancer therapy. This work highlights important considerations associated with preclinical drug response modeling and potential uses of the platform to identify efficacious and preferential sequential therapeutic regimens.

Treatment Tactics in Remission Stage of Episodic Schizophrenia Taking into Account Immunological Parameters

Background: the study of clinical remission in schizophrenia has a significant place at the current stage of development of psychiatric science. Prevention therapy and personalized prophylactic therapy is important to improve the quality of remission, stabilization of the endogenous process and prevention of exacerbation. The aim was to develop apathogenetically grounded method of treatment of episodic schizophrenia at the remission stage using complex assessment of clinical, psychometric and immunological parameters of patients, which allows to control the quality and stability of remission, as well as prediction of exacerbation of the endogenous process. Patients and methods: 91 patients (24 men and 67 women) aged from 18 to 70 years were examined. They were in remission after suffering attacks of an endogenous disease with episodic course (the duration of remissions ranged from 6 months to 12 years). Clinical-psychopathological, psychometric, immunological, clinical and followup, and statistical methods were used. Results: determination of inflammatory and autoimmune markers of blood plasma in the dynamics of the disease and their comparison with the severity of psychopathological symptomatology of patients with schizophrenia, made it possible to form 2 groups of patients with differently directed correlations between the change in clinical state (according to the PANSS scale) and the level of activation of the immune system, assessed in the aggregate immune markers determined. For patients of group 1 (n = 58; 63.74%) the decrease of the intensity of psychopathological symptoms in remission is associated with a decrease in the level of immune system activation («positive» correlations). For the patients of group 2 (n = 33; 36.26%) the level of immune system activation in remission does not decrease and remains at the level of the acute stage of the disease (“negative” correlations). It is shown that the increase in the level of immune system activation in patients of group 1 is a prognostic immunological criterion of possible exacerbation of psychopathological symptoms in remission. Based on the determination of immunological blood parameters in remission, personalized treatment tactics have been developed for these patients, associated with the transition from maintenance doses of drugs to therapeutic ones or additional use of drugs of another group. In most cases, this tactic contributed to relatively favorable dynamics of the existing clinical remission without significant changes in the condition. For patients of group 2, the personalized therapy tactics involves continuation of longterm active therapy. Conclusions: complex assessment of clinical, psychometric and immunological parameters of patients with episodic schizophrenia, which allows to control the quality and stability of remission, can be used to control the quality and stability of remission, as well as to detect preclinical signs of exacerbation of the endogenous process.

Design and development of a mobile health intervention for rehabilitation support after knee arthroplasty: TeleRehabilitation after knee ArThroplasty (TReAT) project

BackgroundThe steep increase in knee arthroplasties in lower- and middle-income countries has increased demand for personalised rehabilitation care. Technology-based rehabilitation programs offer potential to replace or augment conventional face-to-face rehabilitation for providing continuum of care. We aimed to systematically develop a theory, evidence-driven, contextualised, and user-centred mobile health platform for people undergoing knee arthroplasty to facilitate monitoring progress during rehabilitation.MethodsWe followed the UK Medical Research Council’s framework and adopted a behaviour design thinking approach, consisting of five steps; (1) conduct multiple literature reviews and cross-sectional surveys to determine the needs and problems experienced during the rehabilitation phase by healthcare professionals and individuals with replaced knees, (2) identify target behaviours along with barriers and facilitators, integrated within the Capability, Opportunity, Motivation-Behaviour (COM-B) framework, (3) identify intervention functions and components, behaviour change techniques, and features for the mobile application, selected after discussions with orthopaedic surgeons, physiotherapists and behavioural experts, (4) develop a prototype application for end-users, and a web-based platform for healthcare professionals, and (5) pilot test the intervention for end-users’ feedback on usability of the application and to identify possible strategies for implementing the intervention package.ResultsUsing the COM-B model, education, training, persuasion, enablement, and modelling were chosen as intervention functions for improving adherence to exercise protocol during early-phase of rehabilitation. The application featured five interlinked components; education, training, goal setting and self-management, communication, and a personalized therapy plan utilising 13 behaviour change techniques. An education booklet, exercise videos, text messaging, video consultation, and a digital tool for measuring range of motion were incorporated into the application. Feedback on the interactive prototype and education material was sought from ten patient volunteers and a physiotherapist. Pilot testing in a convenient sample with broad eligibility criteria (n = 30) indicated importance of digital literacy and dedicated time for remote consultation by healthcare provider within the existing clinical work. Overall, the intervention was well received and valued by the end users.ConclusionThis paper demonstrates the development of a contextualised digital behavioural intervention grounded in behavioural theory and evidence from literature to improve end-user’s engagement with rehabilitation protocols after knee arthroplasty in lower- and middle-income country context.Trial registrationStudy registered prospectively (on 16 February 2023) at Clinical Trials Registry of India (CTRI/2023/02/049792).

N6-methyladenosine regulators in hepatocellular carcinoma: investigating the precise definition and clinical applications of biomarkers

BackgroundAccurately identifying effective biomarkers and translating them into clinical practice have significant implications for improving clinical outcomes in hepatocellular carcinoma (HCC). In this study, our objective is to explore appropriate methods to improve the accuracy of biomarker identification and investigate their clinical value.MethodsConcentrating on the N6-methyladenosine (m6A) modification regulators, we utilized dozens of multi-omics HCC datasets to analyze the expression patterns and genetic features of m6A regulators. Through the integration of big data analysis with function experiments, we have redefined the biological roles of m6A regulators in HCC. Based on the key regulators, we constructed m6A risk models and explored their clinical value in estimating prognosis and guiding personalized therapy for HCC.ResultsMost m6A regulators exhibit abnormal expression in HCC, and their expression is influenced by copy number variations (CNV) and DNA methylation. Large-scale data analysis has revealed the biological roles of many key m6A regulators, and these findings are well consistent with experimental results. The m6A risk models offer significant prognostic value. Moreover, they assist in reassessing the therapeutic potential of drugs such as sorafenib, gemcitabine, CTLA4 and PD1 blockers in HCC.ConclusionsOur findings suggest that the mutual validation of big data analysis and functional experiments may facilitate the precise identification and definition of biomarkers, and our m6A risk models may have the potential to guide personalized chemotherapy, targeted treatment, and immunotherapy decisions in HCC.

Systematic review of Buzhong Yiqi method in alleviating cancer-related fatigue: a meta-analysis and exploratory network pharmacology approach

ObjectivesCancer-related fatigue (CRF) is a prevalent and distressing symptom experienced by many cancer patients, necessitating effective treatments. This study utilizes meta-analysis and network pharmacology to comprehensively assess the efficacy of the Buzhong Yiqi prescription in alleviating cancer-related fatigue and to preliminarily explore the mechanism of its core drugs.MethodsWe included randomized controlled trials (RCTs) in cancer patients. The inclusion criteria encompassed a diagnosis of cancer-related fatigue, without limitation on cancer type, the experimental group receiving Buzhong Yiqi prescription, the control group receiving conventional treatment, patients awaiting treatment, and articles published in either English or Chinese. We conducted a search through 29 February 2024, across PubMed, Cochrane Database of Systematic Reviews, Cochrane Controlled Clinical Trials (CENTRAL), China Biomedical Literature Service (CBM), China National Knowledge Infrastructure (CNKI), WANFANG Database, and Weipu Database (VIP). Journal articles that met the inclusion criteria were selected for inclusion. Two independent investigators evaluated the quality of the included studies. A meta-analysis was performed utilizing the Stata 12.0 software package, where estimates of cancer-related fatigue were aggregated through the application of a random-effects model. We employed the Cochrane Risk of Bias Tool to evaluate potential biases in RCTs. The primary outcome measures utilized to assess the efficacy and safety of CRF treatment comprised the Revised Piper Fatigue Scale (PFS-R) and the Quality of Life Questionnaire Core 30 (EORTC QLQ-C30). The secondary outcomes encompassed the KPS score, the effective rate, the TCM syndrome score, and an evaluation of adverse reactions. The Traditional Chinese Medicine Systems Pharmacology (TCMSP) was utilized to identify the active ingredients and targets of BZD. Additionally, the Drug bank, Therapeutic Target Database (TTD), DiaGeNET, and GeneCards databases were utilized to retrieve relevant targets for CRC. The Venn diagram was employed to identify overlapping targets. Cytoscape software was utilized to construct a network of “herb-ingredient-target” and identify core targets. GO and KEGG pathway enrichment analyses were performed using R language software.ResultsIn comparison to the control group, patients with CRF who received BZYQ prescription exhibited marked improvements in KPS score, QLQ-C30 quality of life score, and effective rate. Conversely, PFS, TCM syndrome score, and adverse reaction assessments significantly decreased. The primary active ingredients in its core drugs may exert a positive therapeutic effect on CRF by targeting molecules such as AKT1, IL6, IL1B, PTGS2, CASP3, ESR1, and BCL2, as well as through signaling pathways including TNF, IL17, TLR, NF-κB, and C-type lectin receptor.ConclusionBZYQ demonstrates significant efficacy in treating CRF with minimal adverse reactions. It can serve as a fundamental treatment for CRF in clinical practice, and the medication can be tailored to individual patients for personalized therapy. The potential pharmacological mechanism of BZYQ in treating CRF, as predicted by network pharmacology, offers a molecular foundation for clinical CRF treatment.Systematic Review Registration:https://inplasy.com, identifier INPLASY202430025

Computational Analysis of S1PR1 SNPs Reveals Drug Binding Modes Relevant to Multiple Sclerosis Treatment

Background/Objectives: Multiple sclerosis (MS) is an autoimmune disorder of the central nervous system (CNS) characterized by myelin and axonal damage with a globally rising incidence. While there is no known cure for MS, various disease-modifying treatments (DMTs) exist, including those targeting Sphingosine-1-Phosphate Receptors (S1PRs), which play important roles in immune response, CNS function, and cardiovascular regulation. This study focuses on understanding how nonsynonymous single nucleotide polymorphisms (rs1299231517, rs1323297044, rs1223284736, rs1202284551, rs1209378712, rs201200746, and rs1461490142) in the S1PR1’s active site affect the binding of endogenous ligands, as well as different drugs used in MS management. Methods: Extensive molecular dynamics simulations and linear interaction energy (LIE) calculations were employed to predict binding affinities and potentially guide future personalized medicinal therapies. The empirical parameters of the LIE method were optimized using the binding free energies calculated from experimentally determined IC50 values. These optimized parameters were then applied to calculate the binding free energies of S1P to mutated S1PR1, which correlated well with experimental values, confirming their validity for assessing the impact of SNPs on S1PR1 binding affinities. Results: The binding free energies varied from the least favorable −8.2 kcal/mol for the wild type with ozanimod to the most favorable −16.7 kcal/mol for the combination of siponimod with the receptor carrying the F2055.42L mutation. Conclusions: We successfully demonstrated the differences in the binding modes, interactions, and affinities of investigated MS drugs in connection with SNPs in the S1PR1 binding site, resulting in several viable options for personalized therapies depending on the present mutations.