Developmental and epileptic encephalopathies: from genetic heterogeneity to phenotypic continuum.

Developmental and epileptic encephalopathies (DEEs) are a heterogeneous group of disorders characterized by early-onset, often severe epileptic seizures and EEG abnormalities on a background of developmental impairment that tends to worsen as a consequence of epilepsy. DEEs may result from both nongenetic and genetic etiologies. Genetic DEEs have been associated with mutations in many genes involved in different functions including cell migration, proliferation, and organization, neuronal excitability, and synapse transmission and plasticity. Functional studies performed in different animal models and clinical trials on patients have contributed to elucidate pathophysiological mechanisms underlying many DEEs and have explored the efficacy of different treatments. Here, we provide an extensive review of the phenotypic spectrum included in the DEEs and of the genetic determinants and pathophysiological mechanisms underlying these conditions. We also provide a brief overview of the most effective treatment now available and of the emerging therapeutic approaches.

SMART Designs: Bridging the Gap Between Clinical Trials and Practice in Infectious Diseases.

Traditional Randomized Controlled Trials often fall short in addressing the specific needs of clinical practice due to their one-size-fits-all treatment approaches. Sequential Multiple Assignment Randomized Trials (SMARTs) offer a dynamic and adaptive approach, allowing for multiple randomizations based on patient responses and evolving conditions. SMARTs enable personalized treatment pathways, such as in the trial for antiretroviral therapy (ART) in South Africa, which adjusts treatment based on patient outcomes. Despite these advantages, the use of SMARTs in infectious diseases remains limited. Greater adoption of SMARTs could promote more personalized treatment approaches, improve flexibility in response to public health needs, and enhance the effectiveness of interventions. However, challenges such as recruitment and increased expertise needed for more complex analyses must be addressed. Additionally, combining SMARTs with other adaptive designs could further improve the relevance and outcomes of clinical research.

Clinical utility of comprehensive liquid molecular profiling in patients with advanced endometrial cancer.

BACKGROUND: Molecular characterization has significantly improved the management of advanced endometrial cancer (EC). It distinguishes four molecular subclasses associated with prognosis and personalized therapeutic strategies. This study assesses the clinical utility of cell-free DNA (cfDNA) profiling in EC to identify targetable alterations. METHODS: Women with metastatic or recurrent EC were prospectively recruited within the framework of the STING trial (NCT04932525), during which cfDNA was analyzed. Genomic alterations were identified with the FoundationOne CDx assay. Each molecular report underwent review by a molecular tumor board. Alterations were categorized via the European Society of Medical Oncology Scale for Clinical Actionability of Molecular Targets (ESCAT). RESULTS: A total of 61 patients were enrolled. The median age was 66.9 years, with 43% presenting frontline metastatic disease. All histologic subgroups were represented. Notably, 89% of patients yielded informative cfDNA analysis. Six tumors were classified with deficient mismatch repair/microsatellite instability (11%) and 37 as TP53 gene mutant (67%), and 12 had nonspecific molecular profiles (22%). Molecular classification based on liquid biopsy showed 87.5% accuracy in correlating with tissue results. Moreover, 65% of cases exhibited ≥1 actionable alteration, including 25% ESCAT I alterations and 13% ESCAT II alterations. Consequently, 16% of patients received a molecularly matched therapy, and presented with a 56% response rate and median progression-free survival of 7.7 months. CONCLUSIONS: cfDNA sequencing in EC is a feasible approach that produces informative results in 89% of cases and accurately categorizes patients into the main molecular subclasses. It also reveals multiple actionable alterations, which offers the potential for personalized therapeutic strategies.

Histone acetylation risk model predicts prognosis and guides therapy selection in glioblastoma: implications for chemotherapy and anti-CTLA-4 immunotherapy.

BACKGROUND: Glioblastoma is characterized by high aggressiveness, frequent recurrence, and poor prognosis. Histone acetylation-associated genes have been implicated in its occurrence and development, yet their predictive ability in glioblastoma prognosis remains unclear. RESULTS: This study constructs a histone acetylation risk model using Cox and LASSO regression analyses to evaluate glioblastoma prognosis. We assessed the model's prognostic ability with univariate and multivariate Cox regression analyses. Additionally, immune infiltration was evaluated using ESTIMATE and TIMER algorithms, and the SubMAP algorithm was utilized to predict responses to CTLA4 inhibitor. Multiple drug databases were applied to assess drug sensitivity in high- and low-risk groups. Our results indicate that the histone acetylation risk model is independent and reliable in predicting prognosis. CONCLUSIONS: Low-risk patients showed higher immune activity and longer overall survival, suggesting anti-CTLA4 immunotherapy suitability, while high-risk patients might benefit more from chemotherapy. This model could guide personalized therapy selection for glioblastoma patients.

Complete remission following pembrolizumab in a man with mCRPC with both microsatellite instability and BRCA2 mutation.

Prostate cancer is one of the most prevalent malignancies in men. In the United States, 1 in 8 men will be diagnosed with prostate cancer in their lifetime. Specifically, studies have delved into male subgroups that present a heightened risk for prostate cancer. Despite such high prevalence, prostate cancer can be heterogeneous and carry complexities that manifest differently between individuals. Metastatic hormone-sensitive prostate cancer (mHSPC) often has an abbreviated, aggressive disease course, and can have varying presentations with different molecular profiles that determine response/resistance to the approved treatments targeting the androgen-receptor pathway (eg, enzalutamide, apalutamide, darolutamide, and abiraterone acetate). We present a case of mHSPC quickly progressing to mCRPC, found to have microsatellite instability in mCRPC and excellent response to pembrolizumab, which raises the critical issues of early molecular testing and treatments personalized for the individual patient.

Comprehensive characterization of stemness-related lncRNAs in triple-negative breast cancer identified a novel prognostic signature related to treatment outcomes, immune landscape analysis and therapeutic guidance: a silico analysis with in vivo experiments.

BACKGROUND: Cancer stem cells (CSCs) and long non-coding RNAs (lncRNAs) are known to play a crucial role in the growth, migration, recurrence, and drug resistance of tumor cells, particularly in triple-negative breast cancer (TNBC). This study aims to investigate stemness-related lncRNAs (SRlncRNAs) as potential prognostic indicators for TNBC patients. METHODS: Utilizing RNA sequencing data and corresponding clinical information from the TCGA database, and employing Weighted Gene Co-expression Network Analysis (WGCNA) on TNBC mRNAsi sourced from an online database, stemness-related genes (SRGs) and SRlncRNAs were identified. A prognostic model was developed using univariate Cox and LASSO-Cox analysis based on SRlncRNAs. The performance of the model was evaluated using Kaplan-Meier analysis, ROC curves, and ROC-AUC. Additionally, the study delved into the underlying signaling pathways and immune status associated with the divergent prognoses of TNBC patients. RESULTS: The research identified a signature of six SRlncRNAs (AC245100.6, LINC02511, AC092431.1, FRGCA, EMSLR, and MIR193BHG) for TNBC. Risk scores derived from this signature were found to correlate with the abundance of plasma cells. Furthermore, the nominated chemotherapy drugs for TNBC exhibited considerable variability between different risk score groups. RT-qPCR validation confirmed abnormal expression patterns of these SRlncRNAs in TNBC stem cells, affirming the potential of the SRlncRNAs signature as a prognostic biomarker. CONCLUSION: The identified signature not only demonstrates predictive power in terms of patient outcomes but also provides insights into the underlying biology, signaling pathways, and immune status associated with TNBC prognosis. The findings suggest the possibility of guiding personalized treatments, including immune checkpoint gene therapy and chemotherapy strategies, based on the risk scores derived from the SRlncRNA signature. Overall, this research contributes valuable knowledge towards advancing precision medicine in the context of TNBC.

Understanding Patient Experiences to Inform Future Studies to Optimize Personalization of Treatment for Early Breast Cancer.

BACKGROUND: Breast cancer treatment is multimodal, but not all patients benefit from each treatment, and many experience morbidities significantly impacting quality of life. There is increasing interest in tailoring breast cancer treatments to optimize oncological outcomes and reduce treatment burden, but it is vital that future trials focus on treatments that most impact patients. This study was designed to explore patient experiences of treatment to inform future research. METHODS: An online survey was co-developed with patient advocates to explore respondents' experiences of breast cancer treatment. Questions included simple demographics, treatments received, and views regarding omitting treatments if that is deemed safe. The survey was circulated via social media and patient advocacy groups. Responses were summarized by using simple statistics; free text was analyzed thematically. RESULTS: Of the 235 participants completing the survey, 194 (82.6%) would choose to omit a specific treatment if safe to do so. The most commonly selected treatments were chemotherapy (n = 69, 35.6%) and endocrine therapy (n = 61, 31.4%) mainly due to side effects. Fewer respondents would choose to omit surgery (n = 40, 20.6%) or radiotherapy (n = 20, 10.3%). Several women commented that survival was their "absolute priority" and that high-quality evidence to support the safety of reducing treatment would be essential. CONCLUSIONS: Patients with breast cancer are individuals who may wish to optimize different components of their treatment. A portfolio of studies co-designed with patients is needed to establish an evidence base for greater treatment personalization with studies focused on reducing avoidable chemotherapy and endocrine therapy a priority.

Unlocking the full potential of mesenchymal stromal cell therapy for osteoarthritis through machine learning-based in silico trials.

Despite the potential of mesenchymal stromal cells (MSCs) in osteoarthritis (OA) treatment, the challenge lies in addressing their therapeutic inconsistency. Clinical trials revealed significantly varied therapeutic outcomes among patients receiving the same allogenic MSCs but different treatment regimens. Therefore, optimizing personalized treatment strategies is crucial to fully unlock MSCs' potential and enhance therapeutic consistency. We employed the XGBoost algorithm to train a self-collected database comprising 37 published clinical reports to create a model capable of predicting the probability of effective pain relief and Western Ontario and McMaster Universities (WOMAC) index improvement in OA patients undergoing MSC therapy. Leveraging this model, extensive in silico simulations were conducted to identify optimal personalized treatment strategies and ideal patient profiles. Our in silico trials predicted that the individually optimized MSC treatment strategies would substantially increase patients' chances of recovery compared to the strategies used in reported clinical trials, thereby potentially benefiting 78.1%, 47.8%, 94.4% and 36.4% of the patients with ineffective short-term pain relief, short-term WOMAC index improvement, long-term pain relief and long-term WOMAC index improvement, respectively. We further recommended guidelines on MSC number, concentration, and the patients' appropriate physical (body mass index, age, etc.) and disease states (Kellgren-Lawrence grade, etc.) for OA treatment. Additionally, we revealed the superior efficacy of MSC in providing short-term pain relief compared to platelet-rich plasma therapy for most OA patients. This study represents the pioneering effort to enhance the efficacy and consistency of MSC therapy through machine learning applied to clinical data. The in silico trial approach holds immense potential for diverse clinical applications.

Metabolic subtypes and immune landscapes in esophageal squamous cell carcinoma: prognostic implications and potential for personalized therapies.

BACKGROUND: This study aimed to identify metabolic subtypes in ESCA, explore their relationship with immune landscapes, and establish a metabolic index for accurate prognosis assessment. METHODS: Clinical, SNP, and RNA-seq data were collected from 80 ESCA patients from the TCGA database and RNA-seq data from the GSE19417 dataset. Metabolic genes associated with overall survival (OS) and progression-free survival (PFS) were selected, and k-means clustering was performed. Immune-related pathways, immune infiltration, and response to immunotherapy were predicted using bioinformatic algorithms. Weighted gene co-expression network analysis (WGCNA) was conducted to identify metabolic genes associated with co-expression modules. Lastly, cell culture and functional analysis were performed using patient tissue samples and ESCA cell lines to verify the identified genes and their roles. RESULTS: Molecular subtypes were identified based on the expression profiles of metabolic genes, and univariate survival analysis revealed 163 metabolic genes associated with ESCA prognosis. Consensus clustering analysis classified ESCA samples into three distinct subtypes, with MC1 showing the poorest prognosis and MC3 having the best prognosis. The subtypes also exhibited significant differences in immune cell infiltration, with MC3 showing the highest scores. Additionally, the MC3 subtype demonstrated the poorest response to immunotherapy, while the MC1 subtype was the most sensitive. WGCNA analysis identified gene modules associated with the metabolic index, with SLC5A1, NT5DC4, and MTHFD2 emerging as prognostic markers. Gene and protein expression analysis validated the upregulation of MTHFD2 in ESCA. MTHFD2 promotes the progression of ESCA and may be a potential therapeutic target for ESCA. CONCLUSION: The established metabolic index and identified metabolic genes offer potential for prognostic assessment and personalized therapeutic interventions for ESCA, underscoring the importance of targeting metabolism-immune interactions in ESCA. MTHFD2 promotes the progression of ESCA and may be a potential therapeutic target for ESCA.

Translational Connectomics: overview of machine learning in macroscale Connectomics for clinical insights.

Connectomics is a neuroscience paradigm focused on noninvasively mapping highly intricate and organized networks of neurons. The advent of neuroimaging has led to extensive mapping of the brain functional and structural connectome on a macroscale level through modalities such as functional and diffusion MRI. In parallel, the healthcare field has witnessed a surge in the application of machine learning and artificial intelligence for diagnostics, especially in imaging. While reviews covering machine learn ing and macroscale connectomics exist for specific disorders, none provide an overview that captures their evolving role, especially through the lens of clinical application and translation. The applications include understanding disorders, classification, identifying neuroimaging biomarkers, assessing severity, predicting outcomes and intervention response, identifying potential targets for brain stimulation, and evaluating the effects of stimulation intervention on the brain and connectome mapping in patients before neurosurgery. The covered studies span neurodegenerative, neurodevelopmental, neuropsychiatric, and neurological disorders. Along with applications, the review provides a brief of common ML methods to set context. Conjointly, limitations in ML studies within connectomics and strategies to mitigate them have been covered.

Long-term Effect of Multichannel tDCS Protocol in Patients with Central Cortex Epilepsies Associated with Epilepsia Partialis Continua.

Epilepsia partialis continua (EPC) is a rare type of focal motor status epilepticus that causes continuous muscle jerking in a specific part of the body. Experiencing this type of seizure, along with other seizure types, such as focal motor seizures and focal to bilateral tonic-clonic seizures, can result in a disabling situation. Non-invasive brain stimulation methods like transcranial direct current stimulation (tDCS) show promise in reducing seizure frequency (SF) when medications are ineffective. However, research on tDCS for EPC and related seizures is limited. We evaluated personalized multichannel tDCS in drug-resistant EPC of diverse etiologies for long-term clinical efficacy We report three EPC patients undergoing a long-term protocol of multichannel tDCS. The patients received several cycles (11, 9, and 3) of five consecutive days of stimulation at 2 mA for 2 × 20 min, targeting the epileptogenic zone (EZ), including the central motor cortex with cathodal electrodes. The primary measurement was SF changes. In three cases, EPC was due to Rasmussen's Encephalitis (case 1), focal cortical dysplasia (case 2), or remained unknown (case 3). tDCS cycles were administered over 6 to 22 months. The outcomes comprised a reduction of at least 75% in seizure frequency for two patients, and in one case, a complete cessation of severe motor seizures. However, tDCS had no substantial impact on the continuous myoclonus characterizing EPC. No serious side effects were reported. Long-term application of tDCS cycles is well tolerated and can lead to a considerable reduction in disabling seizures in patients with various forms of epilepsy with EPC.

Tailoring brain-machine interface rehabilitation training based on neural reorganization: towards personalized treatment for stroke patients.

Electroencephalogram (EEG)-based brain-machine interface (BMI) has the potential to enhance rehabilitation training efficiency, but it still remains elusive regarding how to design BMI training for heterogeneous stroke patients with varied neural reorganization. Here, we hypothesize that tailoring BMI training according to different patterns of neural reorganization can contribute to a personalized rehabilitation trajectory. Thirteen stroke patients were recruited in a 2-week personalized BMI training experiment. Clinical and behavioral measurements, as well as cortical and muscular activities, were assessed before and after training. Following treatment, significant improvements were found in motor function assessment. Three types of brain activation patterns were identified during BMI tasks, namely, bilateral widespread activation, ipsilesional focusing activation, and contralesional recruitment activation. Patients with either ipsilesional dominance or contralesional dominance can achieve recovery through personalized BMI training. Results indicate that personalized BMI training tends to connect the potentially reorganized brain areas with event-contingent proprioceptive feedback. It can also be inferred that personalization plays an important role in establishing the sensorimotor loop in BMI training. With further understanding of neural rehabilitation mechanisms, personalized treatment strategy is a promising way to improve the rehabilitation efficacy and promote the clinical use of rehabilitation robots and other neurotechnologies.

Systemic osteoarthritis: the difficulty of categorically naming a continuous condition.

Osteoarthritis (OA) is a disease with systemic implications that go beyond joint problems. Its pathogenic mechanisms involve a variety of systemic conditions that contribute to joint damage. These include metabolic dysfunction, chronic low-grade inflammation, neuroplastic pain, and the influence of the central nervous system in the development of neuropathic pain. Besides, OA can negatively affect other aspects of health, such as quality of life, reduced physical activity, social isolation, depression, and anxiety. OA can be considered a complex system in which pathological interactions involve not only obesity and metabolic dysfunction, but also fragility syndrome, sarcopenia, neurological complications, and systemic energy redistribution. Complex systems are composed of multiple interacting and dynamic parts and exhibit emergent properties that cannot be fully explained by examining their individual components. Chronic low-grade inflammation is characteristic of OA, occurring both in the affected joint, and systemically, mainly due to adipose tissue inflammation in obese patients. Obesity is a key factor in the progression of OA, so primary treatment should focus on its control, while maintaining muscle health. The chronic inflammation could lead to changes in energy distribution among the affected joint tissues. Therefore, OA should be approached as a systemic disease, considering individual patient factors, such as genetics, inflammatory response, and lifestyle. Medical care should be more holistic and personalized. Consideration of a name change, such as "systemic OA", could help to move away from the perception of a disease focused only on the joints.

Precision Medicine in Depression: The Role of Proteomics and Metabolomics in Personalized Treatment Approaches.

Depression, or major depressive disorder (MDD), is a widespread mental health condition marked by enduring feelings of sorrow and loss of interest. Treatment of depression frequently combines psychotherapy, medication, and lifestyle modifications. However, the occurrence of treatment resistance in certain individuals makes it difficult for physicians to effectively manage this disorder, calling for the implementation of alternative therapeutic strategies. Recently, precision medicine has gained increased attention in the field of mental health, paving the way for more personalized and effective therapeutic interventions in depression. Also known as personalized medicine, this approach relies on genetic composition, molecular profiles, and environmental variables to customize therapies to individual patients. In particular, precision medicine has offered novel viewpoints on depression through two specific domains: proteomics and metabolomics. On one hand, proteomics is the thorough study of proteins in a biological system, while metabolomics focuses on analyzing the complete set of metabolites in a living being. In the past few years, progress in research has led to the identification of numerous depression-related biomarkers using proteomics and metabolomics techniques, allowing for early identification, precise diagnosis, and improved clinical outcome. However, despite significant progress in these techniques, further efforts are required for advancing precision medicine in the diagnosis and treatment of depression. The overarching goal of this chapter is to provide the current state of knowledge regarding the use of proteomics and metabolomics in identifying biomarkers related to depression. It also highlights the potential of proteomics and metabolomics in elucidating the intricate processes underlying depression, opening the door for tailored therapies that could eventually enhance clinical outcome in depressed patients. This chapter finally discusses the main challenges in the use of proteomics and metabolomics and discusses potential future research directions.

The cellular composition of chronic subdural hematoma.

INTRODUCTION: The pathophysiology of chronic subdural hematoma (CSDH) remains to be fully understood. Basic knowledge of the composition and features of cells in the CSDH fluid may contribute to the understanding of the seemingly complex processes involved in CSDH formation and recurrence. This study is the first to examine the composition of cells and of cellular features in both systemic blood and subdural fluid from CSDH patients. We hypothesized that the cellular composition and features in the hematoma fluid may be; 1) different from that in the systemic blood; 2) different between patients with and without recurrence; 3) and different between the first and second operation in patients with recurrent CSDH. METHODS: Systemic blood and subdural hematoma fluid were collected from CSDH patients with and without recurrent CSDH at the time of primary and secondary surgery. Analyses of cells and cellular features included total number of white blood cells, erythroblasts, reticulocytes, platelets, neutrophilocytes, lymphocytes, monocytes, eosinophils, basophils, reticulocytes, immature granulocytes, mean corpuscular cell volume (MCV), mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, hemoglobin and hematocrit. RESULTS: Of the 85 included patients, 20 patients were operated for a recurrent CSDH within 90 days follow-up. All cells found in the systemic blood were present in the CSDH fluid, but the composition was different (p < 0.0001). MCV was higher in the hematoma fluid from the primary operation of patients later developing a recurrent CSDH compared to patients not developing recurrence (p = 0.009). Also, the percentage distribution of inflammatory cells in hematoma fluid from patients with recurrent CSDH was different between the first and second operation (p = 0.0017). CONCLUSION: This study is the first to investigate the cellular composition of CSDH fluid. Compared to systemic blood and to a reference distribution, an increased number of immune cells were present in the hematoma fluid, supporting an inflammatory component of the CSDH pathophysiology. MCV was higher in the subdural fluid at time of the first operation of CSDH patients later developing recurrence. CLINICAL TRIAL REGISTRATION: The study was approved by the Scientific Ethical Committee of the Capital Region of Denmark (Journal no. H-20051073.

An Advanced Machine Learning Model for a Web-Based Artificial Intelligence-Based Clinical Decision Support System Application: Model Development and Validation Study.

BACKGROUND: Breast cancer is a leading global health concern, necessitating advancements in recurrence prediction and management. The development of an artificial intelligence (AI)-based clinical decision support system (AI-CDSS) using ChatGPT addresses this need with the aim of enhancing both prediction accuracy and user accessibility. OBJECTIVE: This study aims to develop and validate an advanced machine learning model for a web-based AI-CDSS application, leveraging the question-and-answer guidance capabilities of ChatGPT to enhance data preprocessing and model development, thereby improving the prediction of breast cancer recurrence. METHODS: This study focused on developing an advanced machine learning model by leveraging data from the Tri-Service General Hospital breast cancer registry of 3577 patients (2004-2016). As a tertiary medical center, it accepts referrals from four branches-3 branches in the northern region and 1 branch on an offshore island in our country-that manage chronic diseases but refer complex surgical cases, including breast cancer, to the main center, enriching our study population's diversity. Model training used patient data from 2004 to 2012, with subsequent validation using data from 2013 to 2016, ensuring comprehensive assessment and robustness of our predictive models. ChatGPT is integral to preprocessing and model development, aiding in hormone receptor categorization, age binning, and one-hot encoding. Techniques such as the synthetic minority oversampling technique address the imbalance of data sets. Various algorithms, including light gradient-boosting machine, gradient boosting, and extreme gradient boosting, were used, and their performance was evaluated using metrics such as the area under the curve, accuracy, sensitivity, and F1-score. RESULTS: The light gradient-boosting machine model demonstrated superior performance, with an area under the curve of 0.80, followed closely by the gradient boosting and extreme gradient boosting models. The web interface of the AI-CDSS tool was effectively tested in clinical decision-making scenarios, proving its use in personalized treatment planning and patient involvement. CONCLUSIONS: The AI-CDSS tool, enhanced by ChatGPT, marks a significant advancement in breast cancer recurrence prediction, offering a more individualized and accessible approach for clinicians and patients. Although promising, further validation in diverse clinical settings is recommended to confirm its efficacy and expand its use.

Randomized controlled trial of individualized arousal-biofeedback for children and adolescents with disruptive behavior disorders (DBD).

Disruptive behavior disorders [including conduct disorder (CD) and oppositional defiant disorder (ODD)] are common childhood and adolescent psychiatric conditions often linked to altered arousal. The recommended first-line treatment is multi-modal therapy and includes psychosocial and behavioral interventions. Their modest effect sizes along with clinically and biologically heterogeneous phenotypes emphasize the need for innovative personalized treatment targeting impaired functions such as arousal dysregulation. A total of 37 children aged 8-14 years diagnosed with ODD/CD were randomized to 20 sessions of individualized arousal biofeedback using skin conductance levels (SCL-BF) or active treatment as usual (TAU) including psychoeducation and cognitive-behavioral elements. The primary outcome was the change in parents´ ratings of aggressive behavior measured by the Modified Overt Aggression Scale. Secondary outcome measures were subscales from the Child Behavior Checklist, the Inventory of Callous-Unemotional traits, and the Reactive-Proactive Aggression Questionnaire. The SCL-BF treatment was neither superior nor inferior to the active TAU. Both groups showed reduced aggression after treatment with small effects for the primary outcome and large effects for some secondary outcomes. Importantly, successful learning of SCL self-regulation was related to reduced aggression at post-assessment. Individualized SCL-BF was not inferior to active TAU for any treatment outcome with improvements in aggression. Further, participants were on average able to self-regulate their SCL, and those who best learned self-regulation showed the highest clinical improvement, pointing to specificity of SCL-BF regulation for improving aggression. Further studies with larger samples and improved methods, for example by developing BF for mobile use in ecologically more valid settings are warranted.

STASISM: A Versatile Serious Gaming Multi-Sensor Platform for Personalized Telerehabilitation and Telemonitoring.

Telemonitoring and telerehabilitation have shown promise in delivering individualized healthcare remotely. We introduce STASISM, a sensor-based telerehabilitation and telemonitoring system, in this work. This platform has been created to facilitate individualized telerehabilitation and telemonitoring for those who need rehabilitation or ongoing monitoring. To gather and analyze pertinent and validated physiological, kinematic, and environmental data, the system combines a variety of sensors and data analytic methodologies. The platform facilitates customized rehabilitation activities based on individual needs, allows for the remote monitoring of a patient's progress, and offers real-time feedback. To protect the security of patient data and to safeguard patient privacy, STASISM also provides secure data transmission and storage. The platform has the potential to significantly improve the accessibility and efficacy of telerehabilitation and telemonitoring programs, enhancing patients' quality of life and allowing healthcare professionals to provide individualized care outside of traditional clinical settings.

Molecular Classification of Endometrial Cancer and Its Impact on Therapy Selection.

Endometrial cancer (EC) accounts for 90% of uterine cancer cases. It is considered not only one of the most common gynecological malignancies but also one of the most frequent cancers among women overall. Nowadays, the differentiation of EC subtypes is based on immunohistochemistry and molecular techniques. It is considered that patients' prognosis and the implementation of the appropriate treatment depend on the cancer subtype. Patients with pathogenic variants in POLE have the most favorable outcome, while those with abnormal p53 protein have the poorest. Therefore, in patients with POLE mutation, the de-escalation of postoperative treatment may be considered, and patients with abnormal p53 protein should be subjected to intensive adjuvant therapy. Patients with a DNA mismatch repair (dMMR) deficiency are classified in the intermediate prognosis group as EC patients without a specific molecular profile. Immunotherapy has been recognized as an effective treatment method in patients with advanced or recurrent EC with a mismatch deficiency. Thus, different adjuvant therapy approaches, including targeted therapy and immunotherapy, are being proposed depending on the EC subtype, and international guidelines, such as those published by ESMO and ESGO/ESTRO/ESP, include recommendations for performing the molecular classification of all EC cases. The decision about adjuvant therapy selection has to be based not only on clinical data and histological type and stage of cancer, but, following international recommendations, has to include EC molecular subtyping. This review describes how molecular classification could support more optimal therapeutic management in endometrial cancer patients.

Role of microRNAs in Immune Regulation with Translational and Clinical Applications.

MicroRNAs (miRNAs) are 19-23 nucleotide long, evolutionarily conserved noncoding RNA molecules that regulate gene expression at the post-transcriptional level. In this review, involvement of miRNAs is summarized in the differentiation and function of immune cells, in anti-infective immune responses, immunodeficiencies and autoimmune diseases. Roles of miRNAs in anticancer immunity and in the transplantation of solid organs and hematopoietic stem cells are also discussed. Major focus is put on the translational clinical applications of miRNAs, including the establishment of noninvasive biomarkers for differential diagnosis and prediction of prognosis. Patient selection and response prediction to biological therapy is one of the most promising fields of application. Replacement or inhibition of miRNAs has enormous therapeutic potential, with constantly expanding possibilities. Although important challenges still await solutions, evaluation of miRNA fingerprints may contribute to an increasingly personalized management of immune dysregulation with a remarkable reduction in toxicity and treatment side effects. More detailed knowledge of the molecular effects of physical exercise and nutrition on the immune system may facilitate self-tailored lifestyle recommendations and advances in prevention.