Personalized Medicine Approach Research Articles

IL24 Expression in Synovial Myofibroblasts: Implications for Female Osteoarthritis Pain through Propensity Score Matching Analysis

(1) Introduction: Despite documented clinical and pain discrepancies between male and female osteoarthritis (OA) patients, the underlying mechanisms remain unclear. Synovial myofibroblasts, implicated in synovial fibrosis and OA-related pain, offer a potential explanation for these sex differences. Additionally, interleukin-24 (IL24), known for its role in autoimmune disorders and potential myofibroblast production, adds complexity to understanding sex-specific variations in OA. We investigate its role in OA and its contribution to observed sex differences. (2) Methods: To assess gender-specific variations, we analyzed myofibroblast marker expression and IL24 levels in synovial tissue samples from propensity-matched male and female OA patients (each n = 34). Gene expression was quantified using quantitative polymerase chain reaction (qPCR). The association between IL24 expression levels and pain severity, measured by a visual analog scale (VAS), was examined to understand the link between IL24 and OA pain. Synovial fibroblast subsets, including CD45-CD31-CD39- (fibroblast) and CD45-CD31-CD39+ (myofibroblast), were magnetically isolated from female patients (n = 5), and IL24 expression was compared between these subsets. (3) Results: Females exhibited significantly higher expression of myofibroblast markers (MYH11, ET1, ENTPD2) and IL24 compared to males. IL24 expression positively correlated with pain severity in females, while no correlation was observed in males. Further exploration revealed that the myofibroblast fraction highly expressed IL24 compared to the fibroblast fraction in both male and female samples. There was no difference in the myofibroblast fraction between males and females. (4) Conclusions: Our study highlights the gender-specific role of myofibroblasts and IL24 in OA pathogenesis. Elevated IL24 levels in females, correlating with pain severity, suggest its involvement in OA pain experiences. The potential therapeutic implications of IL24, demonstrated in autoimmune disorders, open avenues for targeted interventions. Notwithstanding the limitations of the study, our findings contribute to understanding OA’s multifaceted nature and advocate for future research exploring mechanistic underpinnings and clinical applications of IL24 in synovial myofibroblasts. Additionally, future research directions should focus on elucidating the precise mechanisms by which IL24 contributes to OA pathology and exploring its potential as a therapeutic target for personalized medicine approaches.

The metabolome as a diagnostic for maximal aerobic capacity during exercise in type 1 diabetes.

Our aim was to characterise the in-depth metabolic response to aerobic exercise and the impact of residual pancreatic beta cell function in type 1 diabetes. We also aimed to use the metabolome to distinguish individuals with type 1 diabetes with reduced maximal aerobic capacity in exercise defined by . Thirty participants with type 1 diabetes (≥3 years duration) and 30 control participants were recruited. Groups did not differ in age or sex. After quantification of peak stimulated C-peptide, participants were categorised into those with undetectable (<3 pmol/l), low (3-200 pmol/l) or high (>200 pmol/l) residual beta cell function. Maximal aerobic capacity was assessed by test and did not differ between control and type 1 diabetes groups. All participants completed 45 min of incline treadmill walking (60% ) with venous blood taken prior to exercise, immediately post exercise and after 60 min recovery. Serum was analysed using targeted metabolomics. Metabolomic data were analysed by multivariate statistics to define the metabolic phenotype of exercise in type 1 diabetes. Receiver operating characteristic (ROC) curves were used to identify circulating metabolomic markers of maximal aerobic capacity ( ) during exercise in health and type 1 diabetes. Maximal aerobic capacity ( ) inversely correlated with HbA1c in the type 1 diabetes group (r2=0.17, p=0.024). Higher resting serum tricarboxylic acid cycle metabolites malic acid (fold change 1.4, p=0.001) and lactate (fold change 1.22, p=1.23×10-5) differentiated people with type 1 diabetes. Higher serum acylcarnitines (AC) (AC C14:1, F value=12.25, p=0.001345; AC C12, F value=11.055, p=0.0018) were unique to the metabolic response to exercise in people with type 1 diabetes. C-peptide status differentially affected metabolic responses in serum ACs during exercise (AC C18:1, leverage 0.066; squared prediction error 3.07). The malic acid/pyruvate ratio in rested serum was diagnostic for maximal aerobic capacity ( ) in people with type 1 diabetes (ROC curve AUC 0.867 [95% CI 0.716, 0.956]). The serum metabolome distinguishes high and low maximal aerobic capacity and has diagnostic potential for facilitating personalised medicine approaches to manage aerobic exercise and fitness in type 1 diabetes.

ACE Phenotyping in Human Blood and Tissues: Revelation of ACE Outliers and Sex Differences in ACE Sialylation

Angiotensin-converting enzyme (ACE) metabolizes a number of important peptides participating in blood pressure regulation and vascular remodeling. Elevated ACE expression in tissues (which is generally reflected by blood ACE levels) is associated with an increased risk of cardiovascular diseases. Elevated blood ACE is also a marker for granulomatous diseases. Decreased blood ACE activity is becoming a new risk factor for Alzheimer’s disease. We applied our novel approach—ACE phenotyping—to characterize pairs of tissues (lung, heart, lymph nodes) and serum ACE in 50 patients. ACE phenotyping includes (1) measurement of ACE activity with two substrates (ZPHL and HHL); (2) calculation of the ratio of hydrolysis of these substrates (ZPHL/HHL ratio); (3) determination of ACE immunoreactive protein levels using mAbs to ACE; and (4) ACE conformation with a set of mAbs to ACE. The ACE phenotyping approach in screening format with special attention to outliers, combined with analysis of sequencing data, allowed us to identify patient with a unique ACE phenotype related to decreased ability of inhibition of ACE activity by albumin, likely due to competition with high CCL18 in this patient for binding to ACE. We also confirmed recently discovered gender differences in sialylation of some glycosylation sites of ACE. ACE phenotyping is a promising new approach for the identification of ACE phenotype outliers with potential clinical significance, making it useful for screening in a personalized medicine approach.

Unveiling the Potential of Extracellular Vesicles as Biomarkers and Therapeutic Nanotools for Gastrointestinal Diseases.

Extracellular vesicles (EVs), acting as inherent nanocarriers adept at transporting a range of different biological molecules such as proteins, lipids, and genetic material, exhibit diverse functions within the gastroenteric tract. In states of normal health, they participate in the upkeep of systemic and organ homeostasis. Conversely, in pathological conditions, they significantly contribute to the pathogenesis of gastrointestinal diseases (GIDs). Isolating EVs from patients' biofluids facilitates the discovery of new biomarkers that have the potential to offer a rapid, cost-effective, and non-invasive method for diagnosing and prognosing specific GIDs. Furthermore, EVs demonstrate considerable therapeutic potential as naturally targeted physiological carriers for the intercellular delivery of therapeutic cargo molecules or as nanoscale tools engineered specifically to regulate physio-pathological conditions or disease progression. Their attributes including safety, high permeability, stability, biocompatibility, low immunogenicity, and homing/tropism capabilities contribute to their promising clinical therapeutic applications. This review will delve into various examples of EVs serving as biomarkers or nanocarriers for therapeutic cargo in the context of GIDs, highlighting their clinical potential for both functional and structural gastrointestinal conditions. The versatile and advantageous properties of EVs position them as promising candidates for innovative therapeutic strategies in advancing personalized medicine approaches tailored to the gastroenteric tract, addressing both functional and structural GIDs.

Isolation and Characterization of Fibroblast from Normal and Thalassemia Foreskin

Thalassemia is a genetic blood disorder impacting hemoglobin production, varies in severity, and requires lifelong treatments. The advancement in somatic cell reprogramming through induced pluripotent stem cells (iPSCs) represents a personalized medicine approach that holds promise as a treatment for individuals with thalassemia. One source that could be reprogrammed into iPSCs can be generated from foreskin fibroblast cells. This study aimed to isolate and characterize human fibroblast cells from normal (NFF) and thalassemia (TFF) foreskin surface markers (CD44, CD90, CD105, CD73), and negative lineage (CD45, CD34, CD11b, CD19, HLA-DR). Fibroblast cells were isolated out of normal and thalassemia foreskin using the explant method. Characterization of NFF and TFF isolates was carried out using flow cytometry to disclose the expression of CD90, CD44, CD73, CD105, and negative lineage. Isolation of fibroblast cells from normal and thalassemia foreskin was successfully carried out using the explant method with NFF and TFF characterized as expressing CD90, CD44, CD105, CD73, and negative lineages (CD45, CD11b, CD34, CD19, and HLA-DR). This result could lead to a continuation of reprogramming into iPSCs. HIGHLIGHTS Isolation of fibroblast cells from normal and thalassemia foreskin was successfully carried out and cultured within 21 days The surface marker (CD44, CD73, CD90, CD105), and negative lineage (CD45, CD34, CD11b, CD19, HLA-DR) showed similarity to MSCs Fibroblast cells from the foreskin of normal and thalassemia patients were detected expressing CD90, CD105, CD73, CD44, and negative lineages (CD34, CD11b, CD45, CD19, HLA-DR) GRAPHICAL ABSTRACT

Polycystic ovary syndrome.

Despite affecting ~11-13% of women globally, polycystic ovary syndrome (PCOS) is a substantially understudied condition. PCOS, possibly extending to men's health, imposes a considerable health and economic burden worldwide. Diagnosis in adults follows the International Evidence-based Guideline for the Assessment and Management of Polycystic Ovary Syndrome, requiring two out of three criteria - clinical or biochemical hyperandrogenism, ovulatory dysfunction, and/or specific ovarian morphological characteristics or elevated anti-Müllerian hormone. However, diagnosing adolescents omits ovarian morphology and anti-Müllerian hormone considerations. PCOS, marked by insulin resistance and hyperandrogenism, strongly contributes to early-onset type 2 diabetes, with increased odds for cardiovascular diseases. Reproduction-related implications include irregular menstrual cycles, anovulatory infertility, heightened risks of pregnancy complications and endometrial cancer. Beyond physiological manifestations, PCOS is associated with anxiety, depression, eating disorders, psychosexual dysfunction and negative body image, collectively contributing to diminished health-related quality of life in patients. Despite its high prevalence persisting into menopause, diagnosing PCOS often involves extended timelines and multiple health-care visits. Treatment remains ad hoc owing to limited understanding of underlying mechanisms, highlighting the need for research delineating the aetiology and pathophysiology of the syndrome. Identifying factors contributing to PCOS will pave the way for personalized medicine approaches. Additionally, exploring novel biomarkers, refining diagnostic criteria and advancing treatment modalities will be crucial in enhancing the precision and efficacy of interventions that will positively impact the lives of patients.

Could the mitotic count improve personalized prognosis in melanoma patients?

A number of studies have indicated that the mitotic rate may be a predictive factor for poor prognosis in melanoma patients. The aim of this study was to investigate whether the mitotic rate is associated with other prognostic clinical and anatomopathological characteristics. After adjusting for other anatomopathological characteristics, we then verified the prognostic value of the number of mitoses, determining in which population subgroup this variable may have greater prognostic significance on 3-year mortality. The Veneto Cancer Registry (Registro Tumori del Veneto-RTV), a high-resolution population-based dataset covering the regional population of approximately 4.9 million residents, served as the clinical data source for the analysis. Inclusion criteria included all incident cases of invasive cutaneous malignant melanoma recorded in the RTV in 2015 (1,050 cases) and 2017 (1,205 cases) for which the number of mitoses was available. Mitotic classes were represented by Kaplan-Meier curves for short-term overall survival. Cox regression calculated hazard ratios in multivariable models to evaluate the independent prognostic role of different mitotic rate cut-offs. The results indicate that the mitotic rate is associated with other survival prognostic factors: the variables comprising the TNM stage (e.g., tumor thickness, ulceration, lymph node status and presence of metastasis) and the characteristics that are not included in the TNM stage (e.g., age, site of tumor, type of morphology, growth pattern and TIL). Moreover, this study demonstrated that, even after adjusting for these prognostic factors, mitoses per mm2 are associated with higher mortality, particularly in T2 patients. In conclusion, these findings revealed the need to include the mitotic rate in the histological diagnosis because it correlates with the prognosis as an independent factor. The mitotic rate can be used to develop a personalized medicine approach in the treatment and follow-up monitoring of melanoma patients.

Advances in soft tissue wound care: A Comprehensive Review of Facial traumatic injuries

Soft tissue wound care, particularly in the context of facial traumatic injuries, has undergone significant progress propelled by interdisciplinary collaborations between medical professionals and engineers. This comprehensive review explores the evolution of traditional and advanced techniques for managing soft tissue wounds, emphasizing innovations in scar prevention and the integration of cutting-edge technologies. This review covers the immediate assessment and management of facial traumatic injuries, traditional wound care approaches, and the latest advancements in soft tissue wound healing. Notably, collaborative efforts in biomechanical engineering have led to the development of bioengineered scaffolds, innovative dressings, and customizable implants that promise to revolutionize wound closure and tissue regeneration. The integration of robotics into soft tissue wound care procedures has shown promise in improving precision, reducing invasiveness, and enhancing surgical interventions. Additionally, regenerative technologies, including tissue engineering, 3D bioprinting, and regenerative medicine approaches, offer novel strategies for harnessing the body's natural healing processes. Looking ahead, the future of soft tissue wound care holds exciting prospects, including advancements in technology and imaging for more precise diagnostics, personalized medicine approaches tailored to individual patient profiles, and continued collaboration between medicine and engineering to develop innovative solutions. These future directions are poised to redefine the landscape of soft tissue wound care, offering improved outcomes and quality of life for individuals with facial traumatic injuries and other complex wounds.

Revolutionizing Neurological Disorder Treatment: Integrating Innovations in Pharmaceutical Interventions and Advanced Therapeutic Technologies.

Neurological disorders impose a significant burden on individuals, leading to disabilities and a reduced quality of life. However, recent years have witnessed remarkable advancements in pharmaceutical interventions aimed at treating these disorders. This review article aims to provide an overview of the latest innovations and breakthroughs in neurological disorder treatment, with a specific focus on key therapeutic areas such as Alzheimer's disease, Parkinson's disease, multiple sclerosis, epilepsy, and stroke. This review explores emerging trends in drug development, including the identification of novel therapeutic targets, the development of innovative drug delivery systems, and the application of personalized medicine approaches. Furthermore, it highlights the integration of advanced therapeutic technologies such as gene therapy, optogenetics, and neurostimulation techniques. These technologies hold promise for precise modulation of neural circuits, restoration of neuronal function, and even disease modification. While these advancements offer hopeful prospects for more effective and tailored treatments, challenges such as the need for improved diagnostic tools, identification of new targets for intervention, and optimization of drug delivery methods remain. By addressing these challenges and continuing to invest in research and collaboration, we can revolutionize the treatment of neurological disorders and significantly enhance the lives of those affected by these conditions.

Evolution of tick vaccinology.

Ticks represent a major concern for society worldwide. Ticks are also difficult to control, and vaccines represent the most efficacious, safe, economically feasible and environmentally sustainable intervention. The evolution of tick vaccinology has been driven by multiple challenges such as (1) Ticks are difficult to control, (2) Vaccines control tick infestations by reducing ectoparasite fitness and reproduction, (3) Vaccine efficacy against multiple tick species, (4) Impact of tick strain genetic diversity on vaccine efficacy, (5) Antigen combination to improve vaccine efficacy, (6) Vaccine formulations and delivery platforms and (7) Combination of vaccines with transgenesis and paratransgenesis. Tick vaccine antigens evolved from organ protein extracts to recombinant proteins to chimera designed by vaccinomics and quantum vaccinomics. Future directions will advance in these areas together with other novel technologies such as multiomics, AI and Big Data, mRNA vaccines, microbiota-driven probiotics and vaccines, and combination of vaccines with other interventions in collaboration with regions with high incidence of tick infestations and tick-borne diseases for a personalized medicine approach.

Unveiling clinical applications of bacterial extracellular vesicles as natural nanomaterials in disease diagnosis and therapeutics.

Bacterial extracellular vesicles (BEVs) are naturally occurring bioactive membrane-bound nanoparticles released by both gram-negative and positive bacterial species, exhibiting a multifaceted role in mediating host-microbe interactions across various physiological conditions. Increasing evidence supports BEVs as essential mediators of intercellular exchange, influencing bacterial pathogenicity, disease mechanisms, and modulating the host immune response. However, the extent to which these BEV-mediated actions can be leveraged to predict disease onset, guide treatment strategies, and determine clinical outcomes remains uncertain, particularly in terms of their clinical translation potentials. This review briefly describes BEV biogenesis and their internalisation by recipient cells and summarises methods for isolation and characterization, essential for understanding their composition and cargo. Further, it discusses the potential of biofluid-associated BEVs as biomarkers for various diseases, spanning both cancer and non-cancerous conditions. Following this, we also outline the ongoing human clinical trials of using BEVs for vaccine development. In addition to disease diagnostics, this review explores the emerging research of using natural or engineered BEVs as smart nanomaterials for applications in anti-cancer therapy and bone regeneration. This discussion extends to key factors for unlocking the clinical potential of BEVs, such as standardization of BEVs isolation and characterisation, as well as other hurdles in translating these findings to the clinical setting. We propose that addressing these hurdles through collaborative research efforts and well-designed clinical trials holds the key to fully harnessing the clinical potential of BEVs. As this field advances, this review suggests that BEV-based nanomedicine has the potential to revolutionize disease management, paving the way for innovative diagnosis, therapeutics, and personalized medicine approaches. STATEMENT OF SIGNIFICANCE: Extracellular vesicles (EVs) from both host cells and bacteria serve as multifunctional biomaterials and are emerging in the fields of biomedicine, bioengineering, and biomaterials. However, most of the current studies focus on host-derived EVs, leaving a gap in comprehensive research on bacteria-derived EVs (BEVs). Although BEVs offer an attractive option as nanomaterials for drug delivery systems, their unique nanostructure and easy-to-modify functions make them a potential method for disease diagnosis and treatment as well as vaccine development. Our work among the pioneering studies investigating the potential of BEVs as natural nanobiomaterials, plays a crucial role in both understanding the development of diseases and therapeutic interventions.

Cutaneous targets for topical pain medications in patients with neuropathic pain: individual differential expression of biomarkers supports the need for personalized medicine.

Numerous potential cutaneous targets exist for treating chronic pain with topically applied active pharmaceutical ingredients. This preliminary human skin tissue investigation was undertaken to characterize several key biomarkers in keratinocytes and provide proof-of-principle data to support clinical development of topical compounded formulations for peripheral neuropathic pain syndromes, such as postherpetic neuralgia (PHN). The study intended to identify objective biomarkers in PHN skin on a patient-by-patient personalized medicine platform. The totality of biopsy biomarker data can provide a tissue basis for directing individualized compounded topical preparations to optimize treatment efficacy. Referencing 5 of the most common actives used in topical pain relief formulations (ketamine, gabapentin, clonidine, baclofen, and lidocaine), and 3 well-established cutaneous mediators (ie, neuropeptides, cannabinoids, and vanilloids), comprehensive immunolabeling was used to quantify receptor biomarkers in skin biopsy samples taken from ipsilateral (pain) and contralateral (nonpain) dermatomes of patients with PHN. Epidermal keratinocyte labeling patterns were significantly different among the cohort for each biomarker, consistent with potential mechanisms of action among keratinocytes. Importantly, the total biomarker panel indicates that the enriched PHN cohort contains distinct subgroups. The heterogeneity of the cohort differences may explain studies that have not shown statistical group benefit from topically administered compounded therapies. Rather, the essential need for individual tissue biomarker evaluations is evident, particularly as a means to direct a more accurately targeted topical personalized medicine approach and generate positive clinical results.

A personalized medicine approach identifies enasidenib as an efficient treatment for IDH2 mutant chondrosarcoma

Sarcomas represent an extensive group of malignant diseases affecting mesodermal tissues. Among sarcomas, the clinical management of chondrosarcomas remains a complex challenge, as high-grade tumours do not respond to current therapies. Mutations in the isocitrate dehydrogenase (IDH) 1 and 2 genes are among the most common mutations detected in chondrosarcomas and may represent a therapeutic opportunity. The presence of mutated IDH (mIDH) enzymes results in the accumulation of the oncometabolite 2-HG leading to molecular alterations that contribute to drive tumour growth. We developed a personalized medicine strategy based on the targeted NGS/Sanger sequencing of sarcoma samples (n= 6) and the use of matched patient-derived cell lines as a drug-testing platform. The anti-tumour potential of IDH mutations found in two chondrosarcoma cases was analysed invitro, invivo and molecularly (transcriptomic and DNA methylation analyses). We treated several chondrosarcoma models with specific mIDH1/2 inhibitors. Among these treatments, only the mIDH2 inhibitor enasidenib was able to decrease 2-HG levels and efficiently reduce the viability of mIDH2 chondrosarcoma cells. Importantly, oral administration of enasidenib in xenografted mice resulted in a complete abrogation of tumour growth. Enasidenib induced a profound remodelling of the transcriptomic landscape not associated to changes in the 5mC methylation levels and its anti-tumour effects were associated with the repression of proliferative pathways such as those controlled by E2F factors. Overall, this work provides preclinical evidence for the use of enasidenib to treat mIDH2 chondrosarcomas. Supported by the Spanish Research Agency/FEDER (grants PID2022-142020OB-I00; PID2019-106666RB-I00), the ISC III/FEDER (PI20CIII/00020; DTS18CIII/00005; CB16/12/00390; CB06/07/1009; CB19/07/00057); the GEIS group (GEIS-62); and the PCTI (Asturias)/FEDER (IDI/2021/000027).

Racial inequities and rare CFTR variants: Impact on cystic fibrosis diagnosis and treatment

Cystic fibrosis (CF) has been traditionally viewed as a disease that primarily affects White individuals. The disorder results from mutations in the CF transmembrane conductance regulator (CFTR), with varying incidence of CF reported among Black, Indigenous, and People of Color (BIPOC). However, CF occurs among all races, ethnicities, and geographic ancestries, and BIPOC patients typically exhibit worse clinical outcomes. These populations are more likely to carry rare CFTR variants omitted from newborn screening panels, leading to disparities in care such as delayed diagnosis and treatment. In this study, we present a case-in-point describing an individual of Gambian descent identified with CF. Patient genotype includes a premature termination codon (PTC) (c.2353C > T) and previously undescribed single nucleotide deletion (c.1970delG), arguing against effectiveness of currently available CFTR modulator-based interventions. Strategies for overcoming these two variants will likely include combinations of PTC suppressors, nonsense mediated decay inhibitors, and/or alternative approaches (e.g. gene editing). Investigations such as the present study establish a foundation from which therapeutic treatments may be developed. Importantly, c.2353C > T and c.1970delG were not detected in the patient by traditional CFTR screening panels, which include an implicit racial and ethnic diagnostic bias as these tests are comprised of mutations largely observed in people of European ancestry. We suggest that next-generation sequencing of CFTR should be utilized to confirm or exclude a CF diagnosis, in order to equitably serve BIPOC individuals. Additional epidemiologic data, basic science investigations, and translational work are imperative for improving understanding of disease prevalence and progression, CFTR variant frequency, genotype-phenotype correlation, pharmacologic responsiveness, and personalized medicine approaches for patients with African ancestry and other historically understudied geographic lineages

Can Oral Microbiome Predict Low Birth Weight Infant Delivery?

ObjectivesThis study aimed to identify the oral microbiota factors contributing to low birth weight (LBW) in Chinese pregnant women and develop a prediction model using machine learning. MethodsA nested case-control study was conducted in a prospective cohort of 580 Chinese pregnant women, with 23 LBW cases and 23 healthy delivery controls matched for age and smoking habit. Saliva samples were collected at early and late pregnancy, and microbiome profiles were analyzed through 16S rRNA gene sequencing. ResultsThe relative abundance of Streptococcus was over-represented (median 0.259 vs. 0.116) and Saccharibacteria_TM7 was under-represented (median 0.033 vs. 0.068) in the LBW case group than in controls (p<0.001, p=0.015 respectively). Ten species were identified as microbiome biomarkers of LBW by LEfSe analysis, which included 7 species within the genus of Streptococcus or as part of ‘nutritionally variant streptococci’ (NVS), 2 species of opportunistic pathogen Leptotrichia buccalis and Gemella sanguinis (all LDA score>3.5) as risk biomarkers, and one species of Saccharibacteria TM7 as a beneficial biomarker (LDA= -4.5). The machine-learning model based on these 10 distinguished oral microbiota species could predict LBW, with an accuracy of 82%, sensitivity of 91%, and specificity of 73% (AUC-ROC score 0.89, 95% CI: 0.75-1.0). Results of α-diversity showed that mothers who delivered LBW infants had less stable salivary microbiota construction throughout pregnancy than the control group (measured by Shannon, p=0.048; and Pielou's, p=0.021), however the microbiome diversity did not improve the prediction accuracy of LBW. ConclusionsA machine-learning oral microbiome model shows promise in predicting low-birth-weight delivery. Even in cases where oral health is not significantly compromised, opportunistic pathogens or rarer taxa associated with adverse pregnancy outcomes can still be identified in the oral cavity. Clinical SignificanceThis study highlights the potential complexity of the relationship between oral microbiome and pregnancy outcomes, indicating that mechanisms underlying the association between oral microbiota and adverse pregnancy outcomes may involve complex interactions between host factors, microbiota, and systemic conditions. Using machine learning to develop a predictive model based on specific oral microbiota biomarkers provides a potential for personalized medicine approaches. Future prediction models should incorporate clinical metadata to be clinically useful for improving maternal and child health.

Data-driven personalized medicine approaches to cognitive-behavioral therapy allocation in a large sample: A reanalysis of the ENRICHED study

BackgroundAlthough effective treatments for common mental health problems are available, individual responses to treatments are difficult to predict. Treatment efficacy can be optimized by targeting interventions using individual predictions of treatment outcomes. The aim of this study was to develop a prediction algorithm using data from one of the largest randomized clinical trials on psychological interventions for common mental health problems. MethodsThis is a secondary analysis of the Enhancing Recovery in Coronary Heart Disease study investigating the effectiveness of cognitive behavioral therapy (CBT) and care as usual (CAU) for depression and low perceived social support following acute myocardial infarction. 2481 participants were randomly assigned to CBT and CAU. Baseline social-demographics, depression characteristics, comorbid symptoms, and stress and adversity measures were used to build an algorithm predicting post-treatment depression severity using elastic net regularization. Performance and generalizability of this algorithm were determined in a hold-out sample (n = 1203). ResultsTreatment matching based on predictions in the hold-out sample resulted in inconsistent and small effects (d = 0.15), that were more pronounced for individuals matched to CBT (d = 0.22). We identified a small subgroup of individuals for which CBT did not appear more efficacious than CAU. LimitationsLimitations are a poorly defined CAU condition, a low-severity sample, specific exclusion criteria and unavailability of certain baseline variables. ConclusionsSmall matching effects are likely a realistic representation of the performance and generalizability of multivariable prediction algorithms based on clinical measures. Results indicate that future work and new approaches are needed.

Revolutionizing Pancreatic Cancer Diagnosis and Prognosis through Machine Learning Technology

Abstract: Pancreatic cancer presents significant challenges in terms of early detection and treatment, resulting in poor patient outcomes. This research article explores the application of random forest, a powerful machine learning algorithm, to enhance predictive modeling in pancreatic cancer research. Leveraging a dataset comprising clinical and molecular features, we trained and evaluated random forest models to predict key outcomes such as tumor progression, treatment response, and overall survival. Our findings demonstrate the effectiveness of random forest in accurately stratifying patients based on their prognosis and treatment outcomes. Furthermore, we identified key biomarkers and clinical variables contributing to predictive accuracy, providing valuable insights into the underlying biological mechanisms of pancreatic cancer progression. The integration of random forest into pancreatic cancer research holds promise for improving patient stratification, guiding treatment decisions, and ultimately, advancing personalized medicine approaches in the management of this challenging disease

Advancements in Cortisol Detection: From Conventional Methods to Next-Generation Technologies for Enhanced Hormone Monitoring.

The hormone cortisol, released as the end-product of the hypothalamic-pituitary-adrenal (HPA) axis, has a well-characterized circadian rhythm that enables an allostatic response to external stressors. When the pattern of secretion is disrupted, cortisol levels are chronically elevated, contributing to diseases such as heart attacks, strokes, mental health disorders, and diabetes. The diagnosis of chronic stress and stress related disorders depends upon accurate measurement of cortisol levels; currently, it is quantified using mass spectroscopy or immunoassay, in specialized laboratories with trained personnel. However, these methods are time-consuming, expensive and are unable to capture the dynamic biorhythm of the hormone. This critical review traces the path of cortisol detection from traditional laboratory-based methods to decentralised cortisol monitoring biosensors. A complete picture of cortisol biology and pathophysiology is provided, and the importance of precision medicine style monitoring of cortisol is highlighted. Antibody-based immunoassays still dominate the pipeline of development of point-of-care biosensors; new capture molecules such as aptamers and molecularly imprinted polymers (MIPs) combined with technologies such as microfluidics, wearable electronics, and quantum dots offer improvements to limit of detection (LoD), specificity, and a shift toward rapid or continuous measurements. While a variety of different sensors and devices have been proposed, there still exists a need to produce quantitative tests for cortisol ─ using either rapid or continuous monitoring devices that can enable a personalized medicine approach to stress management. This can be addressed by synergistic combinations of technologies that can leverage low sample volumes, relevant limit of detection and rapid testing time, to better account for cortisol's shifting biorhythm. Trends in cortisol diagnostics toward rapid and continuous monitoring of hormones are highlighted, along with insights into choice of sample matrix.

15 years Ludwig Boltzmann Institute for Hematology and Oncology (LBI HO): achievements and future perspectives

SummaryCancer stem cells, also known as leukemic stem cells (LSC) in the context of leukemias, are an emerging topic in translational oncology and hematology. The Ludwig Boltzmann Institute for Hematology and Oncology (LBI HO) was established in 2008 with the aim to translate LSC concepts into clinical practice. Major specific aims of the LBI HO are to identify LSC in various blood cell disorders and to improve anti-leukemic therapies by establishing LSC-targeting and LSC-eradicating approaches with the ultimate aim to translate these concepts into clinical practice. In addition, the LBI HO identified a number of diagnostic and prognostic LSC markers in various blood cell malignancies. Members of the LBI HO have also developed precision medicine tools and personalized medicine approaches around LSC in applied hematology. As a result, diagnosis, prognostication and therapy have improved in the past 10 years. Major disease models are myeloid leukemias and mast cell neoplasms. Finally, the LBI HO consortium launched several projects in the field of open innovation in science where patient-derived initiatives and their input supported the scientific community. Key aims for the future of the LBI HO are to develop LSC-related concepts and strategies further, with the long-term vision to cure more patients with hematologic malignancies.

Transcriptome free energy can serve as a dynamic patient-specific biomarker in acute myeloid leukemia

Acute myeloid leukemia (AML) is prevalent in both adult and pediatric patients. Despite advances in patient categorization, the heterogeneity of AML remains a challenge. Recent studies have explored the use of gene expression data to enhance AML diagnosis and prognosis, however, alternative approaches rooted in physics and chemistry may provide another level of insight into AML transformation. Utilizing publicly available databases, we analyze 884 human and mouse blood and bone marrow samples. We employ a personalized medicine strategy, combining state-transition theory and surprisal analysis, to assess the RNA transcriptome of individual patients. The transcriptome is transformed into physical parameters that represent each sample’s steady state and the free energy change (FEC) from that steady state, which is the state with the lowest free energy.We found the transcriptome steady state was invariant across normal and AML samples. FEC, representing active molecular processes, varied significantly between samples and was used to create patient-specific barcodes to characterize the biology of the disease. We discovered that AML samples that were in a transition state had the highest FEC. This disease state may be characterized as the most unstable and hence the most therapeutically targetable since a change in free energy is a thermodynamic requirement for disease progression. We also found that distinct sets of ongoing processes may be at the root of otherwise similar clinical phenotypes, implying that our integrated analysis of transcriptome profiles may facilitate a personalized medicine approach to cure AML and restore a steady state in each patient.