Personalized Precision Medicine Research Articles

Integrative Approaches for Advancing Organoid Engineering: From Mechanobiology to Personalized Therapeutics

This research manuscript aims to explore the integration of cutting-edge technologies in the field of organoid engineering for applications in personalized precision medicine. The research investigative exploration will delve into the multifaceted aspects of organoid research, incorporating mechanobiological modulation, ultrasound stimulation, and acoustofluidics to enhance the engineering of organoids. The focus will extend to the development of organoids-on-a-chip platforms with integrated biosensors, providing real-time monitoring capabilities for improved disease modeling and drug testing. Additionally, the manuscript will address the challenges and opportunities associated with large-scale manufacturing of organoids, emphasizing the scalability of regenerative medicine approaches. The proposed research will contribute to the advancement of 3D tissue models, micro physiological systems, and multi-organoid systems, offering a very comprehensive perspective on the potential of these systematic technologies in reshaping the landscape of personalized medicine.

Artificial intelligence (AI) in diagnostic imaging.

In the last few years artificial intelligence (AI) has increasingly become a topic of interest, especially in diagnostic imaging. There are two main expected potential benefits: workflow effectiveness and diagnostic support systems, particularly as far as quality assurance is concerned. To meet these objectives, it is necessary to define what artificial intelligence in medicine means and to discuss which detailed steps should be fulfilled, e. g., for MSK imaging in the daily routine. In addition, this article provides an overview of what is necessary for an efficient IT-based workflow including the clinical question, the choice of modalities and investigation protocols, structured reports, and clinical classification. This is particularly interesting for potential providers, who are keen to apply new soft skills to support imaging diagnostic processes. The use of AI-supported diagnostic imaging could result in a paradigm shift from a modality-oriented to a clinical problem-oriented workflow. In order to streamline trauma, degeneration, inflammation, and oncology-MSK diagnostic imaging, the potential benefits of AI-based workflow optimization should be taken advantage of. The following article describes a five-point plan combining patient expectations and specialized radiological standards with respect to investigation protocols and reports. Moreover, this AI-based strategy could help to improve interdisciplinary networking, e. g., boards etc., and facilitate the required leap in quality towards "personalized precision medicine" for the patient. According to the global discussion, there is a need to point out that it is not currently realistic to replace doctors with AI. · AI as support-system has a paramount clinical impact. · AI in the daily routine could be for work-flow-support (processing) - a physician-replacement is un-realistic yet. · Standardization of work-flow by AI could be an important contribution of quality assurance. · Braunschweig R, Kildal D, Janka R. Artificial intelligence (AI) in diagnostic imaging . Fortschr Röntgenstr 2024; DOI: 10.1055/a-2208-6487.

A novel AllGlo probe-quantitative PCR method for detecting single nucleotide polymorphism in CYP2C19 to evaluate the antiplatelet activity of clopidogrel

CYP2C19 gene has multiple single nucleotide polymorphism (SNP), which is the major determinant for clopidogrel treatment responses. Therefore, CYP2C19 SNP detection is essential for predicting clopidogrel efficacy. Currently, there is still no quick and effective method for routine detection of common CYP2C19 SNPs in clinical laboratories, which is critically needed prior to clopidogrel treatment. AllGlo™ based quantitative PCR was used to develop a novel genotyping method for CYP2C19 SNP detection, termed CyPAllGlo. The performance of CyPAllGlo was compared with that of the commonly used fluorescence in situ hybridization (FISH) method, and the data was verified by DNA sequencing. CyPallGlo was used to identify CYP2C19 polymorphisms in 363 patients with coronary heart disease. The univariate analysis was used to access the antiplatelet efficacy of clopidogrel in patients. The associations between CYP2C19 polymorphisms and clopidogrel efficacy were analyzed. Using CyPAllGlo to detect CYP2C19*2 and CYP2C19*3 alleles was highly specific and fast. The detection limit was approximately 0.07 µg/µl and 0.7 µg/µl for CYP2C19*2 and CYP2C19*3, respectively. The consistency between FISH and CyPAllGlo were 98.07% for CYP2C19*2 and 99.17% for CYP2C19*3. DNA sequencing showed that the accuracy of CyPAllGlo was 100%. The analysis time for the whole CyPAllGlo procedure was approximately 60 min. Univariate analysis showed that the anticoagulation efficacy of clopidogrel was related to patient age, CYP2C19 genotype, metabolic phenotype, and LDL level. The logistic regression analysis showed that the genotype of CYP2C19 and metabolic phenotype was the two risk factors for clopidogrel antiplatelet ineffectiveness. This novel CyPAllGlo is a rapid and accurate method for detection of CYP2C19 SNP. The specificity and consistency of CyPAllGlo are comparable with that of widely used DNA sequencing. These findings provide valuable rapid method for predicting clopidogrel efficacy, which can be quickly translated to improve personalized precision medicine for coronary heart disease treatment.

Nanotechnology‐Empowered Combination Cancer Immunotherapies: Mechanisms, Synergies, and Perspectives

This review is aiming to systematically elucidate the unique role of nanotechnology in optimizing therapeutic modalities for combinatorial cancer immunotherapy, which enables the synergistic integration of multiple treatment strategies. In particular, nanotechnology has enabled the synergistic combination of immunotherapy with physical therapies, chemotherapy, metal therapy, and nucleic acid therapy. In each combination regimen, nanocarriers play multifaceted roles by achieving targeted codelivery of different therapeutics and optimizing each individual treatment modality. This offers new paradigms to guide precision medicine in cancer treatment. Immunotherapy alone is unlikely to achieve personalized precision medicine for cancer, and new treatment modalities are needed in the future. To overcome technical bottlenecks and realize precise regulation of the tumor microenvironment for personalized cancer treatment, it is crucial to develop novel nanosystems with integrated sensing, targeting, and therapeutic functionalities.

Immunotherapy guided precision medicine in solid tumors.

Cancer is no longer recognized as a single disease but a collection of diseases each with its defining characteristics and behavior. Even within the same cancer type, there can be substantial heterogeneity at the molecular level. Cancer cells often accumulate various genetic mutations and epigenetic alterations over time, leading to a coexistence of distinct subpopulations of cells within the tumor. This tumor heterogeneity arises not only due to clonal outgrowth of cells with genetic mutations, but also due to interactions of tumor cells with the tumor microenvironment (TME). The latter is a dynamic ecosystem that includes cancer cells, immune cells, fibroblasts, endothelial cells, stromal cells, blood vessels, and extracellular matrix components, tumor-associated macrophages and secreted molecules. The complex interplay between tumor heterogeneity and the TME makes it difficult to develop one-size-fits-all treatments and is often the cause of therapeutic failure and resistance in solid cancers. Technological advances in the post-genomic era have given us cues regarding spatial and temporal tumor heterogeneity. Armed with this knowledge, oncologists are trying to target the unique genomic, epigenetic, and molecular landscape in the tumor cell that causes its oncogenic transformation in a particular patient. This has ushered in the era of personalized precision medicine (PPM). Immunotherapy, on the other hand, involves leveraging the body's immune system to recognize and attack cancer cells and spare healthy cells from the damage induced by radiation and chemotherapy. Combining PPM and immunotherapy represents a paradigm shift in cancer treatment and has emerged as a promising treatment modality for several solid cancers. In this chapter, we summarise major types of cancer immunotherapy and discuss how they are being used for precision medicine in different solid tumors.

Towards voltammetric point of care detection of leucovorin.

Current healthcare trends have seen an increased emphasis on the move towards personalised precision medicine to tailor treatments to the individual and their response to diseases and disease therapies. This highlighting a transition from traditional "one size fits all" to a more nuanced approach. Despite advancements in fundamental knowledge to facilitate personalised prevision approaches, lack of resources to implement such plans remains one of the largest hurdles to overcome. Monitoring of drug therapies is one key aspect that could aid in the evolution of precision medicine alongside the development of drugs and targeted treatment systems. This contribution illustrates the potential of square wave voltammetry (SWV) as a proof-of-concept for monitoring of circulating blood concentrations of treatment therapies within artificial urine, using leucovorin calcium (LV) as a model cancer therapy drug. A low cost, easy-to-use and portable sensor has been developed and successfully employed for the detection of LV over the linear range 0.5-30 μM which represents the therapeutically relevant concentrations for LV within artificial urine without any prior sample preparation required with a limit of detection of 2.63 μM and initial investigations into saliva and serum as biological matrices. The developed sensor describe herein exhibits a proof-of-concept for the engagement of such electrochemical sensors as point-of-care devices, where the sensors ease of use and removal of time-consuming and complex sample preparation methods will ultimately increase its usability by physicians, widening the avenues where electrochemical sensors could be employed.

Application of the Hollow-Fiber Infection Model to Personalized Precision Dosing of Isoniazid in a Clinical Setting.

The hollow-fiber infection model (HFIM) is a valuable tool for evaluating pharmacokinetics/pharmacodynamics relationships and determining the optimal antibiotic dose in monotherapy or combination therapy, but the application for personalized precision medicine in tuberculosis treatment remains limited. This study aimed to evaluate the efficacy of adjusted antibiotic doses for a tuberculosis patient using HFIM. Model-based Bayesian forecasting was utilized to assess the proposed reduction of the isoniazid dose from 300 mg daily to 150 mg daily in a patient with an ultra-slow-acetylation phenotype. The efficacy of the adjusted 150-mg dose was evaluated in a time-to-kill assay performed using the bacterial isolate Mycobacterium tuberculosis (Mtb) H37Ra in a HFIM that mimicked the individual pharmacokinetic profile of the patient. The isoniazid concentration observed in the HFIM adequately reflected the target drug exposures simulated by the model. After 7 days of repeated dose administration, isoniazid killed 4 log10 Mtb CFU/mL in the treatment arm, while the control arm without isoniazid increased 1.6 log10 CFU/mL. Our results provide an example of the utility of the HFIM for predicting the efficacy of specific recommended doses of anti-tuberculosis drugs in real clinical setting.

Multiple on-line active valves based centrifugal microfluidics for dynamic solid-phase enrichment and purification of viral nucleic acid.

Point of care testing (POCT) of nucleic acids holds significant importance in the realm of infectious disease prevention and control, as well as the advancement of personalized precision medicine. Nevertheless, conventional nucleic acid testing methods continue to face challenges such as prolonged detection times and dependence on extensive specialized equipment and personnel, rendering them unsuitable for point of care applications. Here, we proposed an innovative active centrifugal microfluidic system (ACMS) for automatic nucleic acid extraction, encompassing modules for active valve control and magnetic control. An on-chip centrifugal puncture valve (PV) was devised based on the elastic tolerance differences between silicone membranes and tinfoils to release pre-embedded liquid reagents on demand. Furthermore, we have utilized the returnable valve (RV) technology to accurately control the retention and release of liquids, leveraging the high elastic tolerance of the silicone membrane. By incorporating an online controllable magnetic valve, we have achieved controlled and rapid aggregation and dispersion of magnetic beads. The final chip encapsulates multiple reagents and magnetic beads necessary for nucleic acid extraction. Upon sample addition and loading into the instrument, automated on-chip sample loading and nucleic acid extraction, purification, and collection can be accomplished within 30 minutes, halving the overall operation time and even increasing the efficiency of pseudovirus extraction by three orders of magnitude. Consequently, real-time fluorescence quantitative PCR amplification has successfully detected multiple targets of the SARS-CoV-2 virus (with an impressive detection limit as low as 10 copies per μL), along with targeted sequencing analysis yielding a conformity rate of 99%.

Toward microfluidic continuous-flow and intelligent downstream processing of biopharmaceuticals.

Biopharmaceuticals have emerged as powerful therapeutic agents, revolutionizing the treatment landscape for various diseases, including cancer, infectious diseases, autoimmune and genetic disorders. These biotherapeutics pave the way for precision medicine with their unique and targeted capabilities. The production of high-quality biologics entails intricate manufacturing processes, including cell culture, fermentation, purification, and formulation, necessitating specialized facilities and expertise. These complex processes are subject to rigorous regulatory oversight to evaluate the safety, efficacy, and quality of biotherapeutics prior to clinical approval. Consequently, these drugs undergo extensive purification unit operations to achieve high purity by effectively removing impurities and contaminants. The field of personalized precision medicine necessitates the development of novel and highly efficient technologies. Microfluidic technology addresses unmet needs by enabling precise and compact separation, allowing rapid, integrated and continuous purification modules. Moreover, the integration of intelligent biomanufacturing systems with miniaturized devices presents an opportunity to significantly enhance the robustness of complex downstream processing of biopharmaceuticals, with the benefits of automation and advanced control. This allows seamless data exchange, real-time monitoring, and synchronization of purification steps, leading to improved process efficiency, data management, and decision-making. Integrating autonomous systems into biopharmaceutical purification ensures adherence to regulatory standards, such as good manufacturing practice (GMP), positioning the industry to effectively address emerging market demands for personalized precision nano-medicines. This perspective review will emphasize on the significance, challenges, and prospects associated with the adoption of continuous, integrated, and intelligent methodologies in small-scale downstream processing for various types of biologics. By utilizing microfluidic technology and intelligent systems, purification processes can be enhanced for increased efficiency, cost-effectiveness, and regulatory compliance, shaping the future of biopharmaceutical production and enabling the development of personalized and targeted therapies.

Exploring the potential of dual-sensitive hydrogels for personalized precision medicine applications

BackgroundMelanoma, the uncontrolled accumulation of malignant melanocytes, remains one of the most dangerous and deadly types of skin cancer. Current medical interventions, such as radiation and immunotherapy, are ineffective in treating malignant metastatic melanoma of the lung. Due to the complexity of cancer, abnormalities occur and lead to treatment failure. MethodsIn this study, a novel, dual-reaction hydrogel was composed of a thermo-sensitive type (fundamental) and a pH-sensitive type. In addition, the innovative hydrogel showed thermal reversibility and could liquefy at low temperatures and recover at room temperature. We used Fourier transform infrared spectroscopy, scanning electron microscopy, thermogravimetric analysis (TGA), and rheometer to observe the hydrogel's mechanical properties. Significant findingsResults show the hydrogel had a small pore size, revealing positive interactions between molecular chains. The dual-reactive hydrogel exhibited the least cytotoxicity to B16F10 cells in vitro, indicating great biocompatibility and potential. The hydrogel in microparticles brings several advantages, including a high surface area-to-volume ratio and delivery within microscale structures. Microfluidic devices are promising for producing hydrogel particles because they enable high-precision flow control during microfabrication, resulting in precise size and shape. This study used a microfluidic device to produce hydrogel particles and encapsulate cells for future drug screening applications.

The significance of human papillomavirus integration in carcinogenesis and the development of specific diagnostics and countermeasures

Human papillomavirus (HPV) infection is associated with various tumors, notably in the cervix, oropharyngeal region, and anus. As the disease progresses, integration of the viral genome into the host genome is often observed, yet the means of integration and its intrinsic relationship to carcinogenesis remain unclear. To address this gap, novel sequencing technologies have been developed to enhance the accuracy, intuitiveness, and cost-effectiveness of identifying integration breakpoints. HPV genome integration is thought to induce imbalances or dysfunctions in gene expression, epigenetic changes, chromosomal translocation, and genetic instability. The precise mechanisms underlying the changes in gene expression caused by genome integration offer avenues for tumor risk prediction and prognosis assessment. Personalized precision medicine, grounded in the integration patterns unique to each patient, holds promise for cancer treatment. This review summarizes and discusses the current state of knowledge regarding the mechanisms, carcinogenesis, detection and analysis, and clinical significance of HPV integration. It synthesizes existing information to offer a comprehensive overview, potentially enhancing our understanding of HPV-related tumorigenesis and aiding in the development of more effective diagnostic and therapeutic strategies.

BMC3PM: bioinformatics multidrug combination protocol for personalized precision medicine and its application in cancer treatment

BackgroundIn recent years, drug screening has been one of the most significant challenges in the field of personalized medicine, particularly in cancer treatment. However, several new platforms have been introduced to address this issue, providing reliable solutions for personalized drug validation and safety testing. In this study, we developed a personalized drug combination protocol as the primary input to such platforms.MethodsTo achieve this, we utilized data from whole-genome expression profiles of 6173 breast cancer patients, 312 healthy individuals, and 691 drugs. Our approach involved developing an individual pattern of perturbed gene expression (IPPGE) for each patient, which was used as the basis for drug selection. An algorithm was designed to extract personalized drug combinations by comparing the IPPGE and drug signatures. Additionally, we employed the concept of drug repurposing, searching for new benefits of existing drugs that may regulate the desired genes.ResultsOur study revealed that drug combinations obtained from both specialized and non-specialized cancer medicines were more effective than those extracted from only specialized medicines. Furthermore, we observed that the individual pattern of perturbed gene expression (IPPGE) was unique to each patient, akin to a fingerprint.ConclusionsThe personalized drug combination protocol developed in this study offers a methodological interface between drug repurposing and combination drug therapy in cancer treatment. This protocol enables personalized drug combinations to be extracted from hundreds of drugs and thousands of drug combinations, potentially offering more effective treatment options for cancer patients.

Risk adjustment model for tuberculosis compared to non-tuberculosis mycobacterium or latent tuberculosis infection: Center for Personalized Precision Medicine of Tuberculosis (cPMTb) cohort database

BackgroundThe Center for Personalized Precision Medicine of Tuberculosis (cPMTb) was constructed to develop personalized pharmacotherapeutic systems for tuberculosis (TB). This study aimed to introduce the cPMTb cohort and compare the distinct characteristics of patients with TB, non-tuberculosis mycobacterium (NTM) infection, or latent TB infection (LTBI). We also determined the prevalence and specific traits of polymorphisms in N-acetyltransferase-2 (NAT2) and solute carrier organic anion transporter family member 1B1 (SLCO1B1) phenotypes using this prospective multinational cohort.MethodsUntil August 2021, 964, 167, and 95 patients with TB, NTM infection, and LTBI, respectively, were included. Clinical, laboratory, and radiographic data were collected. NAT2 and SLCO1B1 phenotypes were classified by genomic DNA analysis.ResultsPatients with TB were older, had lower body mass index (BMI), higher diabetes rate, and higher male proportion than patients with LTBI. Patients with NTM infection were older, had lower BMI, lower diabetes rate, higher previous TB history, and higher female proportion than patients with TB. Patients with TB had the lowest albumin levels, and the prevalence of the rapid, intermediate, and slow/ultra-slow acetylator phenotypes were 39.2%, 48.1%, and 12.7%, respectively. The prevalence of rapid, intermediate, and slow/ultra-slow acetylator phenotypes were 42.0%, 44.6%, and 13.3% for NTM infection, and 42.5%, 48.3%, and 9.1% for LTBI, respectively, which did not differ significantly from TB. The prevalence of the normal, intermediate, and lower transporter SLCO1B1 phenotypes in TB, NTM, and LTBI did not differ significantly; 74.9%, 22.7%, and 2.4% in TB; 72.0%, 26.1%, and 1.9% in NTM; and 80.7%, 19.3%, and 0% in LTBI, respectively.ConclusionsUnderstanding disease characteristics and identifying pharmacokinetic traits are fundamental steps in optimizing treatment. Further longitudinal data are required for personalized precision medicine.Trial registrationThis study registered ClinicalTrials.gov NO. NCT05280886.

Impact of gold nanoparticles (AuNPs) on eosinophils isolated from male and female individuals

It is well established that some differences exist between the male and female immune systems. Despites this, a sex-based analysis is not frequently performed in most scientific published reports. Knowing that inflammation is a common undesired effect observed resulting from nanoparticle (NP) exposure, we investigate here how in vitro treatment of gold NPs with a primary size of 20 and 70 nm (AuNP20 and AuNP70, respectively) will alter the biology of human eosinophils isolated from men and women blood. We found that treatment of AuNP70, but not AuNP20, significantly delay apoptosis only in eosinophils isolated from women. AuNPs were found to decrease eosinophil phagocytosis, however, significance was only observed in AuNP20-induced eosinophils isolated from women. The production of IL-8 was significantly increased in response to both AuNPs but only in eosinophils isolated from men and the production of IL-1β was increased in AuNPs-induced eosinophils, although significance was observed only in AuNP70-induced eosinophils isolated from women. We conclude that future studies investigating the toxicity of AuNPs (or other NPs) should include a sex-based analysis, especially if the tested NPs have potential medical applications knowing the increased interest in the development of personalized precision medicine.

Nanosensor Chemical Cytometry: Advances and Opportunities in Cellular Therapy and Precision Medicine.

With the definition of therapeutics now encompassing transplanted or engineered cells and their molecular products, there is a growing scientific necessity for analytics to understand this new category of drugs. This Perspective highlights the recent development of new measurement science on label-free single cell analysis, nanosensor chemical cytometry (NCC), and their potential for cellular therapeutics and precision medicine. NCC is based on microfluidics integrated with fluorescent nanosensor arrays utilizing the optical lensing effect of a single cell to real-time extract molecular properties and correlate them with physical attributes of single cells. This new class of cytometry can quantify the heterogeneity of the multivariate physicochemical attributes of the cell populations in a completely label-free and nondestructive way and, thus, suggest the vein-to-vein conditions for the safe therapeutic applications. After the introduction of the NCC technology, we suggest the technological development roadmap for the maturation of the new field: from the sensor/chip design perspective to the system/software development level based on hardware automation and deep learning data analytics. The advancement of this new single cell sensing technology is anticipated to aid rich and multivariate single cell data setting and support safe and reliable cellular therapeutics. This new measurement science can lead to data-driven personalized precision medicine.

Spatial and Temporal Heterogeneity in Clonal Evolution of Nonsmall-cell Lung Cancer: Implications for Therapy

Abstract Introduction Tumoral heterogeneity has been associated with treatment resistance and failure in patients with cancer. Tumoral heterogeneity can be either intertumoral (static variation of cancer cells) or intratumoral (spatial and temporal variation of cancer cells). Nonsmall-cell lung cancer (NSCLC) is considered a model disease for the study of tumoral heterogeneity, given the emerging evidence about the clinical implications of genetic variations among NSCLC subtypes. This review provides an overview of the etiology, detection, and management methods of intratumoral heterogeneity in NSCLC and discusses their clinical implications. Methods The authors searched biomedical databases (Medline, Scopus, Embase) for studies reporting on intratumoral heterogeneity in NSCLC. Results Intratumoral heterogeneity occurs in single tumors, multiple tumors in the same organs, primary tumors and metastases, and among distinct metastases. Genetic (selective pressure, clonal evolution, genomic instability) and nongenetic pathways (tumor metabolism, hypoxia) precipitate heterogeneity across the spatial and temporal progression of the disease. Proposed classifications are based either on cancer subtypes or mutations detected and metastasis sites. Liquid biopsies (cell-free DNA, circulating tumor cells) combined with imaging (computed tomographies (CTs), positron emission tomography/computed tomographies (PET/CTs)) have a major potential for the continuous minimally invasive monitoring of intratumoral heterogeneity in comparison to conventional biopsies. Targeted therapies have a higher likelihood to induce heterogeneity and resistance, while PD-L1 immunotherapy represents a promising therapeutic strategy. Conclusion Spatial and temporal intratumoral heterogeneity within a single patient sets additional challenges to personalized precision medicine, calling for continuous cellular and molecular-level surveillance and adequate adjustment of the treatment plan.

Circular RNAs in Prostate Cancer: Is it Time to Further Explore Liquid Biopsies?

Although diagnosis and treatment of prostate cancer (PCa) have evolved rapidly in recent years, clinically significant molecular biomarkers are still needed to lower the mortality. Circular RNAs (circRNAs) are a poorly characterized component of PCa transcriptome. Recently, since the development of deep RNA sequencing and novel bioinformatic pipelines, emerging evidence suggests circRNAs to have diverse functions in the development and progression of PCa. Thus, we attempt to summarize the current situation and potential development prospects about the role of circRNAs in PCa liquid biopsies. The role of circRNAs in PCa was summarized by searching the literature related to circRNAs in PubMed in recent years. Deregulation of circRNAs is associated with cell proliferation, apoptosis, cell invasion, migration, as well as metastasis in PCa. Because of the high stability and tissue specificity of circRNAs, with improved detection methodologies, circRNAs may be predictive biomarkers in liquid biopsies. From the perspective of recent research, with the development of high-throughput sequencing and novel bioinformatics tools, knowledge of circRNAs will be further expanded. Improved technologies will make personalized precision medicine less of a paper exercise. It is time to further explore circRNA in liquid biopsies.

The Fifth United Arab Emirates Obesity Conference, November 4–5, 2023, Abu Dhabi, UAE

AbstractThe Fifth United Arab Emirates Obesity Conference was held from November 4 to 5, 2023, in Dusit Thani, Abu Dhabi, UAE. The theme of the conference was “What can we do better in obesity management?” The program included several didactic lectures and a question-and-answer time separately or grouped thematically. Over 2 days, the speakers covered the burden, definitions, and evaluation of obesity, including uncommon chromosomal and genetic syndromes associated with obesity in appropriate cases. It highlighted the importance of policy changes for public health improvement and prevention of obesity, the role of multidisciplinary teams in managing persons with obesity holistically, and present and future therapies, including personalized precision medicine backed up by science.

Dysregulation of humoral immunity, iron homeostasis, and lipid metabolism is associated with multiple sclerosis progression

BackgroundThough most patients with multiple sclerosis (MS) presented earlier on as a relapsing-remitting (RR) disease, disability progression eventually occurred. Uncovering the mechanisms underlying progression may facilitate the unmet need for developing therapies to prevent progression. Benign MS (BMS), a rare form of MS, is the opposite from secondary progressive MS (SPMS) in that it lacks disease progression defined as Expanded Disability Status Scale (EDSS) ≤3 after at least 15 years of disease onset. BMS is characterized by rare and mild relapses with complete remission of clinical symptoms (lower activity of the disease) and lack of progression. Our study aims to identify transcriptomic and immunological differences between BMS and SPMS to unravel the pathogenesis of disease progression. MethodsWe took multi-modal approaches with microarrays, flow cytometry, and lipidomics by three-way comparisons of patients with BMS vs. RRMS (low disease activity vs. moderate or severe activity), RRMS vs. SPMS (continued activity vs. complete transformation into progressive phase) as well as BMS vs. SPMS, matched for age and disease-duration (low disease activity and no progression vs. progression with or without activity). ResultsWe found that patients with RRMS and SPMS have a significantly higher percentage of B cells than those with BMS. BMS shows a different transcriptomic profile than SPMS. Many of the differentially expressed genes (DEGs) are involved in B cell-mediated immune responses. Additionally, long-chain fatty acids (LCFA), which can act as inflammatory mediators, are also altered in SPMS. Overall, our data suggest a role for the dysregulation of B cell differentiation and function, humoral immunity, and iron and lipid homeostasis in the pathogenesis of MS disease progression. ConclusionBMS has a unique transcriptomic and immunological profile compared to RRMS and SPMS. These differences will allow for personalized precision medicine and may ultimately lead to the discovery of new therapeutic targets for disease progression.

Deep Learning Can Predict Bevacizumab Therapeutic Effect and Microsatellite Instability Directly from Histology in Epithelial Ovarian Cancer

Epithelial ovarian cancer (EOC) remains a significant cause of mortality among gynecologic cancers, with the majority of cases being diagnosed at an advanced stage. Before targeted therapies were available, EOC treatment relied largely on debulking surgery and platinum-based chemotherapy. Vascular endothelial growth factors have been identified as inducing tumor angiogenesis. According to several clinical trials, anti–vascular endothelial growth factor-targeted therapy with bevacizumab was effective in all phases of EOC treatment. However, there are currently no biomarkers accessible for regular therapeutic use despite the importance of patient selection. Microsatellite instability (MSI), caused by a deficiency of the DNA mismatch repair system, is a molecular abnormality observed in EOC associated with Lynch syndrome. Recent evidence suggests that angiogenesis and MSI are interconnected. Developing predictive biomarkers, which enable the selection of patients who might benefit from bevacizumab-targeted therapy or immunotherapy, is critical for realizing personalized precision medicine. In this study, we developed 2 improved deep learning methods that eliminate the need for laborious detailed image-wise annotations by pathologists and compared them with 3 state-of-the-art methods to not only predict the efficacy of bevacizumab in patients with EOC using mismatch repair protein immunostained tissue microarrays but also predict MSI status directly from histopathologic images. In prediction of therapeutic outcomes, the 2 proposed methods achieved excellent performance by obtaining the highest mean sensitivity and specificity score using MSH2 or MSH6 markers and outperformed 3 state-of-the-art deep learning methods. Moreover, both statistical analysis results, using Cox proportional hazards model analysis and Kaplan-Meier progression-free survival analysis, confirm that the 2 proposed methods successfully differentiate patients with positive therapeutic effects and lower cancer recurrence rates from patients experiencing disease progression after treatment (P < .01). In prediction of MSI status directly from histopathology images, our proposed method also achieved a decent performance in terms of mean sensitivity and specificity score even for imbalanced data sets for both internal validation using tissue microarrays from the local hospital and external validation using whole section slides from The Cancer Genome Atlas archive.