In recent years, the FDA has approved numerous antitumor drugs that are mutation-based for clinical use. These drugs have improved the precision of treatment and reduced adverse effects and side effects. Personalized therapy is a prominent and hot topic of current medicine and also represents the future direction of development. With the continuous advancements in gene sequencing and high-throughput screening, research and development strategies for personalized clinical drugs have developed rapidly. This review elaborated the recent personalized treatment strategies, which include artificial intelligence, multi-omics analysis, chemical proteomics, and computation aided drug design. These technologies rely on the molecular classification of diseases, the global signaling network within organisms, and new models for all targets, which significantly support the development of personalized medicine. Meanwhile, we summarized chemical drugs such as lorlatinib, osimertinib, and other natural products that deliver personalized therapeutic effects based on genetic mutations. This review also highlights potential challenges in interpreting genetic mutations and combining drugs, while providing new ideas for the development of personalized medicine and pharmacogenomics in cancer study.

Boron Neutron Capture Therapy (BNCT) is a promising binary disease-targeted therapy, as neutrons preferentially kill cells labeled with boron (¹⁰B), which makes it a precision medicine treatment modality that provides a therapeutic effect exclusively on patient-specific tumor spread. Contrary to what is usual in radiotherapy, BNCT proposes cell-tailored treatment planning rather than to the tumor mass. The success of BNCT depends mainly on the sufficient spatial biodistribution of ¹⁰B located around or within neoplastic cells to produce a high-dose gradient between the tumor and healthy tissue. However, it is not yet possible to precisely determine the concentration of ¹⁰B in a specific tissue in real-time using noninvasive methods. Critical issues remain to be resolved if BNCT is to become a valuable, minimally invasive, and efficient treatment. Moreover, functional imaging technologies such as PET can be applied to determine biological information that can be used for the combined-modality radiotherapy protocol for each specific patient. Anyway, not only imaging methods but also proteomics and gene expression methods will facilitate BNCT becoming a modality of personalized medicine. This work provides an overview of the fundamental principles, recent advances, and future directions of BNCT as cell-targeted cancer therapy for personalized radiation treatment.

MSMEs play an important role in the global economy, employing nearly 90% of the workforce and contributing nearly 50% of the world's gross domestic product. However, many MSMEs have difficulty finding and retaining repeat customers, which is one of the main challenges for small businesses. This study aims to find out how customer data can be used to improve marketing strategies and meet customer needs and preferences more effectively. This research also aims to provide solutions and recommendations for MSMEs to improve their marketing through personalizing the experience for their customers. This study uses the SEM-PLS method with a quantitative approach. The population is taken from SMEs operating in the city of Semarang. 100 Samples are determined through Quota Sampling. Personalized marketing involves using customer data to customize the customer experience, which can create a closer relationship between customers and MSMEs. This research is still limited to the effectiveness of Personalized Marketing on customer loyalty in MSMEs. There have been many studies that discuss MSME customer loyalty with the marketing communication method. However, there are not many studies that specifically discuss personalized marketing for MSMEs.

Although suicide is a major public health issue worldwide, we understand little of the onset and development of suicidal behavior. Suicidal behavior is argued to be the end result of the complex interaction between psychological, social and biological factors. Epidemiological studies resulted in a range of risk factors for suicidal behavior, but we do not yet understand how their interaction increases the risk for suicidal behavior. A new approach called network analysis can help us better understand this process as it allows to visualize and quantify complex association between many different symptoms or risk factors. A network analysis of data contain information on suicidal patients can help us understand how risk factors interact and how their interaction is related to suicidal thoughts and behaviour. A network perspective has been successfully applied to the field of depression and psychosis, but not yet to the field of suicidology. In this theoretical article, I will introduce the concept of network analysis to the field of suicide prevention, and offer directions for future applications and studies.

The importance of personalized medicine is growing, since there is an urged need to avoid unnecessary and expensive treatments. In nuclear medicine, the theranostic approach is an established tool for a specific molecular targeting in means of diagnostics and therapy. The visualisation of potential targets can help to predict if a patient would benefit from a particular treatment or not. Thanks to the quick development of radiopharmaceuticals and diagnostic techniques, the use of theranostic agents is constantly rising. In this article important milestones of nuclear therapies and diagnostics in the context of theranostics are highlighted. It begins with the well-known radioiodine therapy in patients with thyroid cancer and then guides through different approaches for the treatment of advanced cancer with targeted therapies. The aim of this review is to provide a summary of background knowledge, current applications and advantages of targeted therapies and imaging in nuclear medicine practice.

Personalized cancer vaccines have emerged as a promising avenue for cancer treatment or prevention strategies. This approach targets the specific genetic alterations in individual patient’s tumors, offering a more personalized and effective treatment option. Previous studies have shown that generalized peptide vaccines targeting a limited scope of gene mutations are ineffective, emphasizing the need for personalized approaches. While studies have explored personalized mRNA vaccines, personalized peptide vaccines have not yet been studied in this context. Pancreatic ductal adenocarcinoma (PDAC) remains challenging in oncology, necessitating innovative therapeutic strategies. In this study, we developed a personalized peptide vaccine design methodology, employing RNA sequencing (RNAseq) to identify prevalent gene mutations underlying PDAC development in a specific patient solid tumor tissue. We performed RNAseq analysis for trimming adapters, read alignment, and somatic variant calling. We also developed a Python program, called GeneFinder, which validates the alignment of the RNAseq analysis. The Python program is freely available to download. Using chromosome number and locus data, GeneFinder identifies the target gene along the UCSC hg38 reference set. Based on the gene mutation data, we developed a personalized PDAC cancer vaccine that targets 100 highly prevalent gene mutations in the individual patient. We predicted peptide-MHC binding affinity, immunogenicity, antigenicity, allergenicity, and toxicity for each epitope. Then, we selected the top 50 and 100 epitopes based on our previously published vaccine design methodology. Finally, we generated pMHC-TCR 3D molecular model complex structures which are freely available fo download. The designed personalized cancer vaccine contains epitopes commonly found in PDAC solid tumor tissue. Our personalized vaccine was composed of neoantigens, allowing for a more precise and targeted immune response against cancer cells. Additionally, we identified mutated genes which were also found in the reference study where we obtained the sequencing data, thus validating our vaccine design methodology. This is the first study designing a personalized peptide vaccine using human patient data to identify gene mutations associated with the specific tumor of interest.

The global aging population has experienced rapid growth in recent decades, leading to an increased prevalence of psychiatric disorders, particularly depression, among older adults. Depression in the elderly is often compounded by chronic physical conditions and various psychosocial factors, significantly impacting their quality of life. This paper explores the epidemiology of geriatric depression, highlighting the influence of gender, race, and socio-economic status on its prevalence. The classification and diagnosis of geriatric depressive disorders, based on ICD-11 and DSM-5 criteria, reveal the complexity of managing these conditions in older adults. Personalized Medicine (PM) emerges as a promising approach, focusing on tailoring treatments to the individual’s genetic, clinical, and environmental characteristics. However, the application of PM in this demographic faces challenges, particularly in the context of pharmaceutical forms. The need for age-appropriate drug delivery systems is critical, given the prevalence of polypharmacy and issues such as dysphagia among the elderly. This study emphasizes the importance of developing patient-centric formulations to enhance the effectiveness of personalized therapy in geriatric patients.

3D printing has significantly transformed the design and manufacture of robotic prostheses, making these devices more accessible, customized, and functional. This paper examines the historical evolution of prosthetic technology, tracing its development from rudimentary mechanical devices to the integration of advanced technologies, such as 3D printing. This innovation has enabled the production of prostheses at lower costs while enhancing their adaptability and performance. The review highlights how 3D printing has driven a disruptive shift in prosthetic customization, and how emerging technologies—including smart materials and artificial intelligence—have expanded the capabilities of prosthetic devices, offering more adaptive and natural movement. However, challenges persist, particularly regarding the need for standardization and infrastructure expansion to ensure equitable access to these technologies. Future research into novel materials and manufacturing techniques holds the potential to improve further the functionality, affordability, and accessibility of prosthetic devices. In conclusion, while 3D printing has marked a significant milestone in the evolution of robotic prosthetics, overcoming existing challenges is essential to realize its global impact and benefits fully.

Human hair is characterized by significant diversity, which is dictated by the individual genetic makeup. It is estimated that up to 70% of the world’s population has textured hair, with a signif-icant proportion also living in areas with high exposure to ultraviolet radiation (UVR). This highlights the unique requirements for textured hair care products that can protect hair shafts from the harmful effects of the environment whilst enhancing the natural features of the fibre. In this study, we investigated the UV-protective effects of hair conditioner formulated from active ingredients, mangiferin, ferulic acid and naringin. The analysis of UV-irradiated straight and textured hair was performed by measurements of hair thickness and darkness, the uptake of the fluorescent dye, spectral absorbance of keratin extracts, ATR-FTIR and SEM. We found that tex-tured hair was more sensitive to UVR-induced changes than straight hair, with the primary mo-lecular targets represented by chemical groups in keratins. Pre-treatment of the hair with condi-tioner had protective effects against structural damage and oxidative stress caused by UVR. These data indicate the importance of the personalized approach to hair research and the formu-lation of hair care products tailored to the individual demands of textured hair types.

Pharmacogenomic (PGx) testing to inform antidepressant medication selection and dosing is gaining attention from healthcare professionals, patients, and payors in Australia. However, there is often uncertainty regarding which test is most suitable for a particular patient. Here, we identified and evaluated the coverage of CYP2D6 and CYP2C19 variants in commercial antide-pressant PGx testing panels in Victoria, a large and ethnically diverse state of Australia. Test characteristics and star alleles tested for both genes were obtained directly from pathology la-boratories offering PGx testing and compared against the Association of Molecular Pathology’s recommended minimum (Tier 1) and extended (Tier 2) allele sets. Although all tests covered the minimum recommended alleles for CYP2C19, this was not the case for CYP2D6. This study em-phasizes that PGx tests might not be suitable for all individuals in Australia due to the limited range of star alleles assessed. Inadequate haplotype coverage may risk misclassification of an individual’s predicted metabolizer phenotype which has ramifications for depression medica-tion selection and dosage. The study underscores the urgent need for greater standardization in PGx testing and emphasizes the importance of considering genetic ancestry when choosing a PGx testing panel to ensure optimal clinical applicability.

Background: Immunologic and neuro-inflammatory pathways have been found to play a major role in the pathogenesis of many neurological disorders such as epilepsy, proposing the use of novel therapeutic strategies. In the era of personalized medicine and in the face of the exhaustion of anti-seizure therapeutic resources it is worth looking at the current or future possibilities that neuroimmunomodulator or anti-inflammatory therapy can offer us in the management of patients with epilepsy. Methods: We performed a narrative review on the recent advances on the basic epileptogenic mechanisms related to the activation of immunity or neuroinflammation with special attention to current and future opportunities for novel treatments in epilepsy. Results: Neuroinflammation can be considered a universal phenomenon and occurs in structural, infectious, post-traumatic, autoimmune, or even genetically based epilepsies. The emerging research developed in recent years has allowed us to identify the main molecular pathways in-volved in these processes. These molecular pathways could constitute future therapeutic targets for epilepsy. Conclusions: Different drugs current or in development have demonstrated their capacity to inhibit or modulate molecular pathways involved in the immunologic or neuroinflammatory mechanisms described in epilepsy. Some of them should be tested in the future as possible antiepileptic drugs.

Discovery of novel variants from data derived from local population provides confident targets for developing biomarkers for personalized medicine. Biomarker discovery would be an important tool in advancing and utilizing the concept of precision and personalized medicine in the clinic. We identified the need to generate high quality sequencing data from local population and understand the pattern of occurrence of variants in colorectal cancer patients. In this report, we used archived samples from Saudi Arabia and used Ampliseq Comprehensive Cancer panel to identify novel somatic variants. We report a comprehensive analysis of next generation sequencing results with a coverage of >300X. We identified 466 novel variants which were previously unreported in COSMIC and ICGC databases. We analyzed the genes associated with these variants in terms of their frequency of occurrence, probable pathogenicity and clinicopathological features. Among pathogenic somatic variants, 174 were identified for the first time in large intestine. APC, RET and EGFR genes were most frequently mutated. Higher number of variants were identified in left colon. Occurrence of variants in ERBB2 was significantly correlated with those of EGFR and ATR genes. Network analyses of the identified genes provide functional perspective of the identified genes and suggest affected pathways and probable biomarker candidates. This report lays the ground work for biomarker discovery and identification of driver gene mutations in local population.

Sound evidence recognizes the microbiota as one of the major players in human health and disease, including cancer. Every human being is an holobiont, a shared human and microbial ecosystem, in which microbial composition is individually set by behaviours and environmental factors during the first years of life. Thereafter it is modulated by diet, physical activity, emotions and drugs (in particularly antibiotics and chemotherapeutics). As a consequence, a shift in medicine is needed toward a more comprehensive practice that takes into account every individual's genoma and, in addition, his or her metagenome, known as microbiome: a "microbiota revolution". As regards breast cancer (BC), a clear link between microbiota and oncogenesis is still to be confirmed. Specific microbes display unique features regulating their host niche in a number of body sites, which can result in an increased risk of cancer; in addition, gut microbiota composition plays a role in immune modulation within the intestinal barrier, affecting local and systemic inflammation, recognized drivers of cancer. Moreover, part of the bacterial gene mass inside the gut, constituting the so called “estrobolome”, influences the sexual hormonal balance and subsequentely may impact on the onset, progression and treatment of hormonal dependent cancers. Microbiota is also clearly involved in modulating the response to anticancer treatments, and above all to the emerging immunotherapy. Based on these premises, the microbiome is becoming a potential target, in order to enhance efficacy of antitumoral treatments as well as to lower their toxicity. The complex scenario that links microbiome composition to oncogenesis and response to anticancer treatments defines the frames of a new “oncobiotic” perspective.

Background: We conducted research on the newly developed personalized recommendation service (PRS) of the global network of Korean scientists and engineers (KOSEN) in order to explore the information usage behavior and importance of the PRS used by Korean scientists and engineers. Methods: In order to understand information usage behavior, we gathered data from 513 survey results and analyzed them in terms of information usage behavior and the corresponding importance in each of the service quality areas. Results: We analyzed the 321 outcomes that indicated non-use of the PRS in order to understand the underlying reason(s); we employed 192 results that demonstrated the use of functionality to examine information usage behavior and importance. They found that the predominant motive for non-use of the service resulted from the respondents not knowing how to use it. According to demographic characteristics, the usage behavior of the PRS showed a difference regarding the purpose of using the service in the categories of gender and major field of study. Furthermore, users were concerned with various components of the PRS such as ease of use, design, relevance of content, user support, and interactivity. Conclusions: We suggest reinforcing user education degree and promotion to enhance the PRS. Since users were concerned with ease of use, design, relevance, user support, and interactivity, we recommend these as major points for improvement.

Fluoropyrimidines (FPs) are commonly prescribed in many cancer streams. The DPYD*2A (rs3918290), *13 (rs55886062), *HapB3 (rs56038477), alleles, and DPYD rs67376798, had shown the highest level of evidence about their association with FPs response. The EMA and FDA-approved drug labels for FPs, recommend genotyping these variants before treatment starts. We implemented the DPYD genotyping in our daily clinical routine, but we still find patients showing severe adverse drug events (ADEs) to FPs. We studied among these patients the association with the response to FPs of DPYD rs1801265, rs17376848, rs1801159, rs1801160, rs1801158, and rs2297595; as explanatory candidates of the interindividual differences for the FP-related toxicities. We also studied the impact of DPYD*2A, *13, *HapB3, and rs67376798 genotype for FP dose tailoring in our clinical practice and characterized the DPYD gene in our population. We found that FP dose lowering based on DPYD genotype does not affect the treatment efficacy. Also, the DPYD*4 (defined by rs1801158) allele was associated with a higher risk of ADEs (severity grade ≥ 3) in both the univariate (O.R.= 5.66; 95% C.I.= 1.35 – 23.67; p= 0.014) and multivariate analyses (O.R.= 5.73; 95% C.I.= 1.41 – 28.77; p= 0.019). This makes it a candidate variant for implementation in clinical practice.

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 remote monitoring of patients' 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/

Glioblastoma (GBM) is one of the most aggressive and intricate forms of brain cancer, demanding novel therapeutic interventions. The role of transcription factors in epigenetic regulation has emerged as a promising avenue for targeting GBM. This comprehensive review seeks to explore the complex interplay between specific transcription factors such as SNAI2, FOXA1, YAP1, TWIST1, ZEB1, and NF-kB, and their role in GBM progression and therapy. The focus extends to innovative therapeutic strategies, including epigenetic modifiers, targeted therapies, and CRISPR/Cas9 technology. A unique aspect of this review highlights the connection between transcription factors, neoantigen presentation, and personalized immunotherapy in GBM. By delving into current research, clinical trials, and immunological data, this review emphasizes the significant potential of targeting transcription factors for personalized immunotherapy in GBM. The challenges, future perspectives, and ethical considerations are also critically discussed, offering a complete and thought-provoking insight into a vital and rapidly evolving field.

The microbiota is now recognized as one of the major players in human health and disease, in-cluding cancer. As regards breast cancer (BC), a clear link between microbiota and oncogenesis is still to be confirmed. Yet, part of the bacterial gene mass inside the gut, constituting the so called “estrobolome”, influences the sexual hormonal balance and, since the increased exposure to estrogens is associated with an increased risk, it may impact on the onset, progression, and treatment of hormonal dependent cancers, which account for more than 70% of all BCs. The hormonal dependent BCs are also affected by environmental and dietary endocrine disruptors and phytoestrogens which interact with microbiota in a bidirectional way: on one side disruptors can alter the composition and functions of the estrobolome, on the other the gut microbiota influences the metabolism of endocrine active food components. This review highlights the current evidence about the complex interplay between endocrine disruptors, phytoestrogens, microbiome, and BC, within the frames of a new “oncobiotic” perspective.

In today’s Web 2.0 era, online social media has become an integral part of our lives. In the course of the information revolution, the form of information has undergone a radical change, from simple text information to today’s integrated video, image, text and audio, and there has also been a great change in the way of dissemination and access, as people nowadays do not just rely on traditional media to passively receive information, but more actively and selectively obtain information from social media. Therefore, it has become a great challenge for us to effectively utilize these massive and integrated multi-modal media information to form an effective system of retrieval, browsing, analysis and usage. Unlike movies and traditional long-form video content, micro-videos are usually short in length, between a few seconds and tens of seconds, which allows users to quickly browse different contents and make full use of the fragmented time in their lives, while users can also share their micro-videos to their friends or the public, forming a unique social way. Video contains rich multimodal information, and fusing information from multiple modalities in a video recommendation task can improve the accuracy of the video recommendation task.According to the micro-video recommendation task, a new combinatorial network model is proposed to combine the discrete features of each modality into the overall features of various modalities through the network, and then fuse the various modal features to obtain the overall video features, which will be used for recommendation. In order to verify the effectiveness of the algorithm proposed in this paper, experiments are conducted in the public dataset, and it is shown the effectiveness of our model.

In recent years, lung ultrasound (LUS) has been increasingly used for the diagnosis of respiratory diseases in both adult and pediatric patients. However, asthma is a field in which the use of LUS is not yet well defined or is in development. In the following case series, we describe clinical, laboratory, radiological results as well as detailed lung ultrasound findings of 6 children with asthma: some of them with acute asthma attack and with inadequately controlled allergic asthma or childhood asthma; others with acute asthma and allergic or infantile asthma adequately controlled by preventive therapy. Finally we describe the clinical, laboratory and imaging parameters of a child with severe allergic asthma in the absence of exacerbation. In these cases, albeit at different times, LUS played an important role in both the initial diagnostic process and follow-up. It also showed different ultrasound features depending on the severity of the individual asthma based on the type of asthmatic phenotype and control of it.

Traditional classroom teaching model can no longer meet the needs of students' ability development. As a new model actively advocated in the modern education concept, personalized teaching focuses on the development needs of students and emphasizes respect for students' individualized growth. In this work, we utilize deep learning method to recommend teaching strategies for different features of students. Specifically, the proposed method includes the construction of a multi-layer neural network model, which can analyze students' multi-dimensional learning data and behavior patterns, so as to recommend the most suitable teaching strategies for individual students. Through data collection and preprocessing, model architecture design, model training and optimization, the model gradually improves the accuracy of prediction and recommendation. Experimental analysis verifies the effectiveness of the model through field teaching experiments, and the results show that the personalized teaching strategy based on deep learning can significantly improve the learning effect and participation of students.

Inherited cardiomyopathies are a diverse group of heart muscle diseases caused by genetic mutations that result in structural and functional abnormalities of the myocardium. Understanding genotype-phenotype correlations in these conditions is vital for personalized cardiac medicine, enabling targeted therapeutic strategies and predictive diagnostics. This review explores the major types of inherited cardiomyopathies—hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM), arrhythmogenic right ventricular cardiomyopathy (ARVC), and restrictive cardiomyopathy (RCM)—and provides detailed insights into how different genetic mutations manifest as clinical features. The integration of multi-omics approaches and advanced disease modeling techniques has enhanced our ability to dissect these correlations. The review also discusses the implications of these findings for personalized medicine, including tailored therapeutic strategies, predictive diagnostics, and future research directions.

Lung cancer, including both non-small cell lung cancer and small cell lung cancer, remains the leading cause of cancer-related mortality worldwide representing 18% of the total cancer deaths in 2020. Many patients are identified already at an advanced stage with metastatic disease and have a worsening prognosis. Recent advances in the genetic understanding of lung cancer have opened new avenues for personalized treatments and targeted therapies. This review examines the latest discoveries in the genetics of lung cancer, discusses key biomarkers, and analyzes current clinical therapies based on this genetic information. It will conclude with a discussion of future prospects and potential research directions.

To decrease privacy risks, consumers may choose to misrepresent when they are asked to offer personal information. This paper examines the impact of consumer misrepresentation on a monopolistic firm and consumers with an economic model. The results show that consumer misrepresentation may benefit the firm but hurt consumers. In addition, consumers misrepresentation may encourage the firm to provide higher personalized service level in certain scenarios, such as, when the unit cost of personalized service is low. Thirdly, when consumers misrepresent, and the firm only covers part of the market, a greater unit value of consumer’s privacy information will reduce the firm’s profit, but a greater unit cost of personalized service increases the firm’s profit.

Radiotheranostics refers to pairing of radioactive imaging biomarkers with radioactive therapeutic compounds that deliver ionizing radiation. Given the introduction of very promising radio-pharmaceuticals, the radiotheranostics approach is creating a novel paradigm in personalized, targeted radionuclide therapies (TRTs), also known as radiopharmaceuticals (RPTs). Radiother-apeutic pairs targeting somatostatin receptors (SSTR) and prostate-specific membrane antigens (PSMA) are increasingly used to diagnose and treat patients with metastatic neuroendocrine tumors (NETs) and prostate cancer. In parallel, radiomics and artificial intelligence (AI) as important areas in quantitative image analysis are paving the way for significantly enhanced workflows in diagnostic and theranostic fields, from data and image processing to clinical decision support, improving patient selection, personalized treatment strategies, response prediction, and prognostication. Furthermore, AI has the potential for tremendous effectiveness in patient dosimetry which copes with complex and time-consuming tasks in the RPT workflow. The present work provides a comprehensive overview of radiomics and AI application in radio-theranostics, focusing on pairs of SSTR- or PSMA‑targeting radioligands, describing the funda-mental concepts and specific imaging/treatment features. Our review includes ligands radio-labeled by 68Ga, 18F, 177Lu, 64Cu, 90Y, and 225Ac. Specifically, contributions by radiomics and AI towards improved image acquisition, reconstruction, treatment response, segmentation, restag-ing, lesion classification, dose prediction, and estimation as well as ongoing developments and future directions are discussed.

Telemedicine has been increasingly considered an effective strategy to support patients with several diagnosis and therapies, avoiding the on-site visit and drastically reducing the risks to share infections. In the recent years, a number of different technologies have been developed and applied to clinical studies, with the aim to investigate new ways for increase the early diagnoses and monitor the clinical evolution of the most predictable diseases directly from the patients’ home. Telemedicine refers to all those technological interfaces used to remotely perform clinical procedures. Telemedicine applications could be useful in several medical fields, such as preventive medicine, home patient care, education for healthcare professionals and patients, research, public health, and health management. On the other hand, the synergy of these different technologies has a significant impact on the clinical risks analysis, particularly useful in frail patients. Although Telemedicine shows to have important advantages, undoubtedly important issues still remain. The present work aims to shed light on the main advantages and disadvantages related to of each application’s features, which may serve as a useful tool for researchers.

Personalized surgery is not just a new trend but rather a patient-specific approach to therapy that makes it possible to adopt a targeted approach for a specific patient and closely mirrors the approach of personalized medicine. However, the application of tailored surgery in the context of cardiovascular replacement surgery was not systematically reviewed. The ability to customize a device is highly dependent on the collection of radiological image data for precise prosthesis modelling. These facts are essential to "tailor-made" device design for precise prosthesis implantation. According to this study, computed tomography (CT) was the most prominent imaging modality; however, transoesophageal echocardiography and echocardiography were also found to be helpful. Additionally, a dynamic finite element simulation is also an attractive alternative to the finite element analysis for an in-silico experiment. Nonetheless, there is a paucity of relevant publications and sporadic evidence. More clinical studies have been warranted, notwithstanding that the derived data and results from the insight into the use of therapeutic interventions may be of multiple directives in clinical practices and beyond. This study may help the integration of personalized devices for better comprehending predicted clinical outcomes, thus leading towards enhanced performance gains.

The global prevalence of type 2 diabetes mellitus (T2DM) has surged in recent decades and the identification of differential glycemic responders can aid tailored treatment for prevention of pre-diabetes and T2DM. A mixed meal tolerance test (MMTT) based on regular foods offers potential to uncover differential responders in dynamical postprandial events. We aimed to fit a simple mathematical model on dynamic postprandial glucose data from repeated MMTTs among partic-ipants with elevated T2DM risk, to identify response clusters and investigate their association with T2DM risk factors and gut microbiota. Data were used from a 12-week multi-center dietary inter-vention trial involving high-risk T2DM adults, comparing high- versus low-glycemic index foods within a Mediterranean diet context (MEDGICarb). Model-based analysis of MMTTs from 155 participants (81 females, 74 males) revealed two distinct plasma glucose response clusters that were associated with baseline gut microbiota. Cluster A, inversely associated with HbA1c and waist circumference and directly with insulin sensitivity, exhibited a contrasting profile to cluster B. Findings imply that a standardized breakfast MMTT using regular foods could effectively distin-guish non-diabetic individuals at varying risk levels for T2DM using a simple mechanistic model.

Metabolomics is an advanced technology, still under development, with multiple research applications, especially in the field of health. Individual metabolic profiles, the functionality of the body, as well as its interaction with the environment can be established using this technology. The body's response to various external factors, including the food consumed and the nutrients it contains, has increased researchers' interest in nutrimetabolomics. Establishing correlations between diet and the occurrence of various diseases or even the development of personalized nutrition plans, could contribute to advances in precision medicine. The interdependence between humans, animals and the environment are of particular importance today, with the dramatic emergence and spread of zoonotic diseases, food, water and soil contamination, and the degradation of resources and habitats. All these events have led to an increase of risk factors for functional diseases, burdening global health. Thus, this study aimed to highlight the importance of metabolomics, in particular of nutrimetabolomics, as a technical solution for a holistic, collaborative, and precise approach for the advancement of the One Health strategy.

Energy is needed by cancer cells to stay alive and communicate with their surroundings. The primary organelles for cellular metabolism and energy synthesis are mitochondria. Researchers recently proved that cancer cells can steal immune cells' mitochondria using nanoscale tubes. This finding demonstrates the dependence of cancer cells on normal cells for their living and function. It also denotes the importance of mitochondria in cancer cells’ biology. Emerging evidence has demonstrated how mitochondria are essential for cancer cells to survive in the harsh tumor microenvironment, evade the immune system, obtain more aggressive features, and resist treatments. For instance, functional mitochondria can improve cancer resistance against radiotherapy by scavenging the released reactive oxygen species. Therefore, targeting mitochondria can potentially enhance oncological outcomes, according to this notion. The patients' reactions to radiation are varied, ranging from a complete response to even cancer progression during treatment. This concept illustrates how different levels of mitochondrial metabolism might contribute to this heterogeneity. Considering this notion can help to improve personalized oncological treatments. This article outlines the importance of mitochondrial metabolism in cancer biology and personalized treatments.

CKD in HF patients is very common condition, their dysfunction is closely linked and influence each other, so their management required multidisciplinary and personalized approaches. The diagnosis of HF and CDK relies on signs and symptoms. Several tools, such as blood-based biomarkers and echography help us to clarify and discriminate the main characteristics of these patients. Evidence in improving survival due to new drug-employment in HF, has increasingly challenged physicians to manage patients with multiple diseases, especially in patients with CKD. The difficulty is in the safe administration of these drugs in patients with HF and CKD. Knowing up to which values ​​of creatinine or renal clearance any drug can be administered is fundamental. We wanted to summarize, on this sizable and complex topic, the experiences of various prior study to get clearer ideas and a more precise reference about the assessment and management of HF and CKD.

Text-to-image tasks have gained significant progress, where vivid images are generated from detailed text descriptions. However, existing studies overlook scenarios where the provided text is sparse, neglect the investigation of human preferences, and fail to acknowledge the diversity of aesthetic opinions. To address those issues, we develop a methodology called personalized short-text-to-image generation through aesthetic assessment and human insights. We develop a Personality Encoder (PE) to extract personal information and establish the Big-Five personality traits-based Image Aesthetic Assessment model (BFIAA) for predicting specific human aesthetic preferences. Utilizing the BFIAA model, we fine-tune the Stable Diffusion model to align it more closely with human preferences. Our experiments demonstrate that our BFIAA model can truly reflect human aesthetic preference and the adapted generation model can generate personalized images more preferred by humans.

Background: Radiotherapy in the head-and-neck area is one of the main curative treatment options. However, this comes at the cost of varying levels of normal tissue toxicity, affecting up to 80% of patients. Mucositis can cause pain, weight loss and treatment delay leading to worse outcomes and decreased quality of life. Therefore, there is an urgent need for an approach to predict normal mucosa response in patients prior to treatment. Methods: We here describe an assay to detect irradiation response in patient healthy oral mucosa tissue. Mucosa specimens from the oral cavity were obtained after surgical resection, cut into thin slices, irradiated, and cultured for three days. Seven samples were irradiated with X-ray and three additional samples were irradiated with both X-ray and protons. Results: Healthy oral mucosa tissue slices maintained normal morphology and viability for three days. We measured a dose-dependent response to X-ray irradiation and compared X-ray and proton irradiation in the same mucosa sample, using standardized automated image analysis. Furthermore, increased levels of inflammation inducing factors - major drivers in mucositis development - could be detected after irradiation. Conclusion: This model can be utilized for investigating mechanistic aspects of mucositis development and can be developed into an assay to predict radiation-induced toxicity in normal mucosa.

In recent years, the scientific and technological developments led to an explosion of available videos on the web, increasing the necessity of fast and effective video analysis and summarization. Video summarization methods aim to generate a synopsis by selecting the most informative parts of the video content. The user’s personal preferences, often involved in the expected results, should be taken into account in the video summaries. In this paper, we provide the first comprehensive survey on personalized video summarization relevant to the techniques and datasets used. In this context, we classify and review personalized video summary techniques based on the type of personalized summary, on criteria, on the video domain, on the source of information, on the time of summarization, and on the machine learning technique. Depending on the type of methodology used by the personalized video summarization techniques for the summary production process, we classify the techniques into five major categories, which are feature-based video summarization, key frame selection, shot selection-based approach, video summarization using trajectory analysis, and personalized video summarization using clustering. We also compare personalized video summarization methods and present 37 datasets used to evaluate personalized video summarization methods. Finally, we analyze opportunities and challenges in the field and suggest innovative research lines.

Sweat rate and electrolyte losses have a large inter-individual variability, a personalized approach to hydration can overcome this issue to meet an individual’s needs. This study aimed to investigate the effects of personalized hydration strategy (PHS) on fluid balance and intermittent exercise performance. Twelve participants conducted 11 laboratory visits including a VO2max test and two 5-day trial arms under normothermic (NOR) or hyperthermic (HYP) environmental conditions. Each arm began with 3-day of familiarization exercise following by two random exercise trials with a either PHS or control (CON). Then, participants crossed-over to the second arm for: NOR+PHS, NOR+CON, HYP+PHS, and HYP+CON. The PHS was prescribed according to participants’ fluid and sweat sodium losses. CON drank ad-libitum of commercially-available electrolyte solution. Exercise trials consisted of two phases: 1) 45min constant workload; 2) high-intensity intermittent exercise (HIIT) until exhaustion. PHS had a significantly greater fluid intake compared to CON. HYP+CON produced the lowest sweat sodium concentration. HYP+PHS had a lower thirst perception and a longer HIIT compared to HYP+CON. Thus, PHS reinforces fluid intake and successfully optimizes hydration status, regardless of environmental conditions. PHS may be or is an important factor to preventing negative physiological consequences during high-intensity exercise in the heat.

This article focuses on artificial intelligence in educational technology, starting with an introduction to educational technology, an interdisciplinary field of study that covers the design, development, utilization, and evaluation of technology and digital tools in educational Settings. A detailed description of its definition in an academic context - a multidisciplinary field of computer science and cognitive science that deals with the development of computational systems that exhibit intelligent behaviour, describing its areas of coverage and scope of application. It then introduces the benefits of AI in education technology, specifically addressing personalized learning, adaptive learning systems, automated scoring and feedback, virtual tutors and chatbots, data analytics, as well as content recommendations and natural language processing, accessibility and inclusion. Then it introduces the main concepts of AI personalized learning: AI personalized learning uses the power of artificial intelligence to meet the unique needs and preferences of individual learners, as well as its key principles and main characteristics. Adaptive learning systems harness the power of artificial intelligence and data analytics to tailor the learning experience to each student's needs and abilities, as well as its core principles and strengths. It also introduces the main operating principles of automated grading and feedback: it uses artificial intelligence and computer algorithms to evaluate and evaluate student assignments, tests and exams without the direct involvement of human graders, and its associated benefits. Secondly, the main concepts of virtual tutor are introduced: Virtual tutor is a computer-based system that uses artificial intelligence and machine learning algorithms to provide students with personalized and interactive educational support and its key characteristics and main advantages, and then the nature and benefits of chatbots and help in the field of education. The final conclusion summarizes the benefits and future challenges of integrating AI into educational technology.

Despite recent advances in optimizing reliever, controller and maintenance therapy in asthma, a large subgroup of asthmatic patients continues to suffer from severe asthma, affecting their psychosociological status, their quality of life and even their life expectancy. Identifying and recognizing the specific phenotypic and endotypic characteristics in asthma has provided the medical community to drive the development of targeted therapies that are proved to benefit patients in terms of symptom control, exacerbation rate, quality of life and lung function. Adopting a more tailored and targeted approach is proving to be the key factor in reforming severe asthma treatment, minimizing the patients’ burden, as well as the socioeconomical impact worldwide. In this review, we are going to describe the different phenotypes and endotypes of asthma, the currently approved targeted therapies in severe asthma as imprinted in Global Initiative for Asthma (GINA) 2023, as well as other potential pharmacological agents and strategies investigated or currently under investigation for the treatment of severe asthma.

Background: MOST-plus is a multicenter, randomized, open-label, adaptive Phase II trial evaluating the clinical benefit of targeted treatments matched to molecular alteration in advanced/metastatic solid tumors. Sorafenib was tested on patients with tumors harboring sorafenib-targeted genes Methods: The MOST-plus trial used a randomized discontinuation design. After 12 weeks of sorafenib (400 mg, po BID), patients with progressive disease dis-continued study, patients with objective response were proposed to continue sorafenib, whereas patients with stable disease (SD) were randomly assigned (1:1) to maintenance or interruption of treatment. Primary endpoint was RE-CIST version 1.1 progression-free rate at 16 weeks after randomization (PFR-16w). Secondary endpoints included progression-free survival (PFS), overall survival (OS), and toxicity. Statistical analyses used a sequential Bayes-ian approach with interim efficacy analyses. The enrolment could be stopped in the case of a 95% probability for the estimated PFR-16w to be higher in the maintenance than in the interruption arm (NCT02029001). Results: 151 patients were included, of whom 35 had SD at 12 weeks of Soraf-enib. For the 35 patients with SD on sorafenib, the PFR-16w was 65% [95% credibility interval 43.4–83.7] in the continuation arm and 25% [7.8–48.1] in the interruption arm. Median PFS and OS were improved in maintenance versus interruption arm (mPFS: 5.6 [95%CI 1.97–6.77] months versus 2.0 [95%CI 1.61–3.91] months (p =0.0231) and mOS : 14.3 [95%CI 8.9–23.8] versus 8.0 months [95%CI 3.5–15.2] (p =0.0857)). Conclusion: Sorafenib showed activity in progressive patients with solid tu-mors harboring somatic genomic alterations in sorafenib-targeted genes. Con-tinuing sorafenib when SD is achieved improves PFR compared to interruption

The use of machine learning (ML) approaches to target clinical problems is called to revolutionize clinical decision-making. The success of these tools is subjected to the understanding of the intrinsic processes being used during the classical pathway by which clinicians make decisions. In a parallelism with this pathway, ML can have an impact at four levels: for data acquisition, predominantly by extracting standardized, high-quality information with the smallest possible learning curve; for feature extraction, by discharging healthcare practitioners from performing tedious measurements on raw data; for interpretation, by digesting complex, heterogeneous data in order to augment the understanding of the patient status; and for decision support, by leveraging the previous step to predict clinical outcomes, response to treatment or to recommend a specific intervention. This paper discusses the state-of-the-art, as well as the current clinical status and challenges associated with each of these tasks, together with the challenges related to the learning process, the auditability/traceability, the system infrastructure and the integration within clinical processes.

The value of landscape, as part of collective heritage, can be acquired by GIS due to the multilayer approach of the spatial configuration. Proficiency in geospatial technologies in order to collect, process, analyze, interpret, visualize and communicate geographic information is being increased by undergraduate and graduate students, but in particular by those who are training to become geography teachers at secondary education. This training can be carried out through personalized learning and distance learning methodology. Personalized GIS education aims to integrate students and enhance their understanding of landscape. Some teaching experiences are shown whereby opportunities offered by WebGIS will be described, through quantitative tools and techniques that will allow this modality of learning and improve its effectiveness. Results of this research show that students, through geospatial technologies, learn landscape as a diversity of elements but also the complexity of physical and human factors involved. Several conclusions will be highlighted: i) the contribution of geospatial training to education for sustainable development; ii) spatial analysis as a mean of skills acquisition about measures for landscape conservation; iii) expanding and applying acquired knowledge to other geographic spaces and different landscapes.

Recent advances in organoid technology have revolutionized cancer biology and thera-peutic interventions, offering personalized immunotherapy treatment. Organoids, three-dimensional cell cultures derived from patient tumors, accurately replicate the tumor microenvi-ronment, providing unprecedented insights into tumor-immune interactions and therapeutic re-sponses. In this literature-based study, we discuss various culture methods for the diverse appli-cations of organoids in cancer immunotherapy, including drug screening, personalized treatment strategies, and mechanistic studies. Additionally, we address the technological challenges asso-ciated with these methods and propose potential future solutions to accelerate the development of novel immunotherapeutic approaches. This review highlights the transformative potential of or-ganoid models in advancing preclinical cancer immunotherapy modeling, screening, and evalua-tion, paving the way for more effective and personalized cancer treatments.

Mass spectrometry (MS) has revolutionized clinical chemistry, offering unparalleled capabilities for biomolecule analysis. This review explores the growing significance of MS, particularly when coupled with liquid chromatography (LC-MS), in identifying disease biomarkers and quantifying biomolecules for diagnostic and prognostic purposes. The unique advantages of MS in accurately identifying and quantifying diverse molecules have positioned it as a cornerstone in personalized medicine advancement. MS-based technologies have transformed precision medicine, enabling comprehensive understanding of disease mechanisms and patient-specific treatment responses. LC-MS has shown exceptional utility in analyzing complex biological matrices, while high-resolution MS has expanded analytical capabilities, allowing detection of low-abundance molecules and elucidation of complex biological pathways. The integration of MS with other techniques, such as ion mobility spectrometry, has opened new avenues for biomarker discovery and validation. As we progress towards precision medicine, MS-based technologies will be crucial in addressing the challenges of individualized patient care, driving innovations in disease diagnosis, prognosis, and treatment strategies.

Leucine zipper-EF-hand containing transmembrane protein 1 (LETM1) is a mitochondrial inner membrane protein involved in mitochondrial morphology and homeostasis. LETM1 is highly expressed in many human solid cancers and correlates with poor prognosis possibly due to mitochondrial dysfunction. However, the functional role of LETM1 in the progression of hematological malignancies remains to be elucidated. In this study, we found that higher LETM1 expression was associated with a short overall survival in Diffuse Large B-Cell Lymphoma (DLBCL) and in Acute Myeloid Leukemias (AML) patients. The transcriptomic analysis showed that LETM1 expression is positively correlated with oncogenic pathways (e.g., glucose transport, stem cell maintenance, mTOR and Wnt signaling pathways, mitotic G2/M phase transition, and cell proliferation) and negatively correlated with autophagy-related processes and apoptosis. In contrast, low expression of LETM1 associated with upregulation of BECLIN-1-dependent mitophagy, which in turn improves the prognosis in both DLBCL and AML patients. Our data suggest that LETM1 is a valuable prognostic marker and a possible therapeutic target for improving the clinical outcome in hematological malignancies.

The National Institute of Health Stroke Scale (NIHSS) is used worldwide to classify stroke severity as ‘mild’, ‘moderate’ or ‘severe’ based on neurological impairment. Yet stroke survivors argue that the classification of ‘mild’ does not represent the holistic experience and impact of stroke on their daily lives. In this observational cohort study, we aimed to identify different types of impairment among stroke survivors classified as mild. We used mild stroke survivors’ data from the START longitudinal stroke cohort (n=73) with measures related to sensorimotor, cognition, depression, functional disability, physical activity, work and social adjustment over 12 months. Given the multi-source, multi-granular and unlabelled nature of data, we utilised a structure-adapting, unsupervised machine learning approach, the Growing Self-organising Map (GSOM) algorithm to generate distinct clinical profiles. These diverse impairment profiles revealed that mild stroke survivors experience varying degrees of impairment and impact (cognitive, depression, physical activity, work/social adjustment) at different time points, despite the uniformity implied by their NIHSS-classified ‘mild’ stroke. This emphasises the necessity of creating a holistic and comprehensive representation of mild stroke survivors’ needs over the first-year post-stroke to improve rehabilitation and post-stroke care.

The integration of Artificial Intelligence (AI) in healthcare has transformed clinical practices by improving the accuracy of diagnosis, optimizing surgical procedures, improving the patient experience, and accelerating drug development. This article provides a comprehensive overview of the many applications of artificial intelligence (AI) across several industries, including fast diagnostics, where ML algorithms greatly enhance the accuracy and velocity of disease diagnosis. State-of-the-art robotic-assisted minimally invasive surgical procedures that shorten patients' hospitalization and improve their chances of recovery, cutting-edge AI applications in healthcare monitoring, and medication development. The article also looks at the primary challenges that AI in healthcare will inevitably encounter, such as differences in product quality, a shortage of skilled workers, privacy and ethical issues, and the need for improved regulatory frameworks. Despite the challenges, AI contributes significant advantages to the healthcare industry, providing novel and remarkable contributions to the efficiency of medical procedures and the progress of medical outcomes. The paper emphasizes the importance of cooperation in overcoming current challenges and enhancing the acceptability of AI technology in clinical settings. This will ensure that AI-driven innovations continue to enhance the standards of patient care.

Emerging molecular and computational techniques may allow construction and computational treatment of dynamic digital twins on the scale of populations to individuals. This approach may pave the way for predictive, preventive, and personalized treatment. Because digital twins are independent of geographical location, this may also contribute to global equitable health.

Methylation is a biochemical process involving the addition of a methyl group (-CH3) to various chemical compounds. It plays a crucial role in maintaining the homeostasis of the endothelium, which lines the interior surface of blood vessels and has been linked, among other conditions, to coronary artery disease (CAD). Despite significant progress in CAD diagnosis and treatment, intensive research continues into genotypic and phenotypic CAD biomarkers. This review explores the significance of the methylation pathway and folate metabolism in CAD pathogenesis, with a focus on endothelial dysfunction resulting from deficiency in the active form of folate (5-MTHF). We discuss emerging areas of research into CAD biomarkers and factors influencing the methylation process. By highlighting genetically determined methylation disorders, particularly the MTHFR polymorphism, we propose the potential use of the active form of folic acid (5-MTHF) as a novel CAD biomarker and personalized pharmaceutical for selected patient groups. Our aim is to improve the identification of individuals at high risk of CAD and enhance their prognosis.

Arguably the most important parameter in treating cognitive decline associated with Alzheimer’s disease is the length of time in which improvement, if achieved at all, is sustained. However, monotherapies such as donepezil and memantine are associated with a more rapid decline than no treatment in patients over multi-year follow-ups {Kennedy, 2018 #535}. Furthermore, anti-amyloid antibody treatment, which at best simply slows decline, is associated with accelerated cerebral atrophy [2], resulting in earlier dementia-associated brain volumes for those treated at the MCI stage than untreated patients. In contrast, a precision medicine approach, in which the multiple potential drivers of cognitive decline are identified for each patient and then targeted with a personalized protocol (such as ReCODE), has led to documented improvement in patients with cognitive decline [3]; [4]; [5], but long-term follow-up (>5 years) has not been reported previously. Therefore, we report here sustained cognitive improvement, in some cases for over a decade, in patients treated with a precision medicine protocol—something that has not been reported in patients treated with anti-cholinesterase, glutamate receptor inhibitory, anti-amyloid, or other therapeutic methods. These case studies warrant long-term cohort studies to determine how frequently such sustained cognitive improvements occur in patients treated with precision medicine protocols.

Federated learning trains a neural network model using the client's data to maintain the benefits of centralized model training, while maintaining the privacy. However, if the client data are not independently and identically distributed (non-IID) because of different environments, the accuracy of the model may suffer from client drift during training owing to discrepancies in each client's data. This study proposes a personalized federated learning algorithm based on the concept of multitask learning to divide each client model into two layers: a feature extraction layer and a category prediction layer. The feature extraction layer maps the input data to a low-dimensional feature vector space. Furthermore, the parameters of the neural network are aggregated with those of other clients using an adaptive method. The category prediction layer maps low-dimensional feature vectors to the label sample space, with its parameters remaining unaffected by other clients to maintain client uniqueness. The proposed personalized federated learning method produces faster learning model convergence rates and higher accuracy rates for the non-IID datasets in our experiments.

Osteoarthritis (OA) poses a growing challenge for the aging population, especially in hip and knee joints, contributing significantly to disability and societal costs. Conventional clinical evaluations, typically performed in controlled settings, face difficulties in capturing the dynamic characteristics of rehabilitation and functioning in real-life scenarios. The study assessed the use of automated unsupervised evaluations in the rehabilitation process for patients after hip and knee replacements. We analyzed data from 1144 individuals who used a digital application after surgery, activity data were collected using the Garmin Vivofit 4. Several parameters such as the Peak 6-Minute Consecutive Cadence (P6MC) and Peak 1-Minute Cadence (P1M) were computed and analyzed on a daily basis. The results indicated that cadence measurements, specifically, can effectively differ among individuals with hip and knee conditions as well as the recovery process. Moreover, these metrics demonstrated a high level of responsiveness in monitoring the progress of recovery, surpassing the effectiveness of simply measuring the total number of steps taken. The study highlighted the capacity of technology-driven, patient-centered rehabilitation to improve accuracy, tailor therapy to individual patients, and increased patient involvement. This research also highlighted the significance of automated, unsupervised assessments in enhancing rehabilitation results for OA patients following hip and knee replacement, as tele-rehabilitation becomes increasingly important.

Background: COVID-19 was initially identified as a respiratory system disorder, but it has been discovered to interact with and influence the cardiovascular system. COVID-19-associated cardiovascular (CV) complications are common, resulting in high acute phase mortality and a large number of morbidities in the chronic phase, thus severely impacting patients' quality of life and health outcomes. Nevertheless, clinical, cellular, and molecular biological factors underlying the pathophysiology of cardiovascular complications associated with COVID-19 are poorly understood. Objective: This review investigates putative underlying clinical factors as well as cellular and molecular biological mechanisms by which COVID-19 leads to acute CV complications, including state-of-the-art genomic sequencing-based findings, assessing the long-term CV consequences of COVID-19, and aiming to shed light on developing strategies for differential diagnosis, risk prognostic stratification, prevention, and clinical management of CV sequlea in COVID-19 patients. Methods: For this purpose, a through review of literature and published data was carried out from first report of COVID-19 till October 2023 to find out a comprehensive account of clinical, cellular and molecular genetic factors underlying COVID-19-associated cardiovascular diseases. Results: We found that the relationship between COVID-19 and CV risk is complex and multifaceted. In addition to acute COVID-19 detertriuos effects, COVID-19 survivors may experience long-term CV effects. We provide a detailed account of the involvement of a large number of genomic alterations, microRNAs, and novel viral as well as host proteins in CVDs associated with COVID-19, which has helped identify some novel drug targets to treat COVID-19-related cardiovascular complications. Conclusions: The relationship between COVID-19 and CV risk is complex and multifaceted. While COVID-19 primarily affects the respiratory system, it can also significantly affect CV health. as compared to classical cardiovascular diseases, there are new clinical, cellular and molecular biological factors in CVDs related to COVID-19, that need specific diagnostic assays, prognostic stratification and treatment modules. Therefore, specail care is must taken to treat cardiovascular diseases associated with COVID-19.

Patient value is an important factor in clinical decision-making, but conventionally, it is not incorporated in the decision processes. In this paper, a value-based therapy recommendation comprehensive model is proposed. A literature analysis is conducted to collect value-based evidence. Categorized values and candidate therapies are used in combination as filtering keywords to build this evidence. Then a formalism model is put forward to integrate the value-based evidence with clinical evidence. The relative importance of two evidence can be adjusted dynamically by decision-makers during the decision-making processes. A prototype system was implemented using a case study for breast cancer and validated for feasibility and effectiveness.

The inter-individual variability of CYP450s enzyme activity may be reduced by comparing the effects of bariatric surgery on CYP-mediated drug elimination in comparable patients before and after surgery. The current research will use a low-dose phenotyping cocktail to simultaneously evaluate the activities of six CYP isoforms and P-gp.The results showed that following weight reduction after surgery, the activity of all enzymes increased compared to the obese period, which was statistically significant in the case of CYP3A, CYP2B6, CYP2C9, and CYP1A2. Furthermore, the activity of P-gp after surgery decreased without reaching a statistical significance (p-value>0.05). Obese individuals had decreased CYP3A and CYP2D6 activity than the control group, albeit only CYP3A was statistically important. In addition, there was a trend to increase activity for CYP1A2, CYP2B6, CYP2C9, and CYP2C19 in obese patients compared to the control group, without reaching statistical insignificance (p-value≥0.05). After six months (at least), all enzymes and the P-gp pump activity were significantly higher than the control group except for CYP2D6.Ultimately, a greater comprehension of phenoconversion can aid in altering the patient's treatment. Further studies are required to confirm the changes in the metabolic ratios of probes after bariatric surgery to demonstrate the findings' clinical application. As a result, the effects of inflammation-induced phenoconversion on medication metabolism may differ greatly across persons and drug CYP pathways. It is essential to apply these results to the clinic to recommend dose adjustments.

The glucose-insulin regulatory system and its glucose oscillations is a recurring theme in the literature because of its impact on human lives, mostly the ones affected by diabetes mellitus. Several approaches were proposed, from mathematical to data-based models, with the aim of modeling the glucose oscillation curve. Having such a curve, it is possible to predict, when injecting insulin in type 1 diabetes (T1D) individuals. However, the literature presents prediction horizons no longer than 6 hours, which could be a problem considering their sleeping time. This work presents Tesseratus, a model that adopts a multi-agent approach to combine machine learning and mathematical modeling to predict the glucose oscillation up to 8 hours. Tesseratus uses the pharmacokinetics of insulins and data collected from T1D individuals. Its outcome can support endocrinologists while prescripting daily treatment for T1D individuals, and provide personalized recommendations for such individuals, to keep their glucose concentration in the ideal range. Tesseratus brings pioneering results for prediction horizons of 8 hours for nighttime, in an experiment with seven real T1D individuals. It is our claim that Tesseratus will be a reference for classification of glucose prediction model, supporting the mitigation of short- and long-term complications in the T1D individuals.

The polygenic and multifactorial nature of many psychiatric disorders has hampered the personalized medicine approach implementation in clinical practice. However, induced pluripotent stem cell (iPSC) technology has emerged as an innovative tool for patient-specific disease modeling to expand the pathophysiology knowledge and treatment perspectives in the last decade. Current technologies enable adult human somatic cell reprogramming into induced pluripotent stem cells (iPSCs) to generate neural cells and direct neural cell conversion to model organisms that exhibit phenotypes close to human diseases, thereby effectively representing relevant aspects of neuropsychiatric disorders. iPSCs reflect patient pathophysiology and pharmacological responsiveness, particularly when cultured under conditions that recapitulate spatial tissue organization in brain organoids. Recently, the application of iPSCs has been frequently associated with gene editing that targets the disease-causing gene to deepen the illness pathophysiology and conduct drug screening. Moreover, gene editing has provided a unique opportunity to repair the putative causative genetic lesions in patient-derived cells. Here, we review the use of iPSC technology to model and potentially treat neuropsychiatric disorders by illustrating the key studies on a series of mental disorders, including schizophrenia, major depression disorder, bipolar disorder, and autism spectrum disorder. The future perspective will involve the development of organ-on-a-chip platforms that control the microenvironmental conditions to reflect individual pathophysiological by adjusting physiochemical parameters according to personal health data. This strategy could open new ways to build a disease model that considers individual variability and tailors personalized treatments.

The new CFTR modulator combination, elexacaftor/tezacaftor/ivacaftor (Trikafta) was approved by the FDA in October 2019 for treatment of Cystic Fibrosis in patients 12 years of age or older who have at least one F508del mutation in one allele and a minimal-function or another F508del mutation in the other allele. However, there is a group of patients, in addition to those with rare mutations, in which despite the presence of a F508del in one allele, it was not possible to identify any mutation in the other allele. Today these patients are excluded from treatment with Trikafta. In Italy CF patients carrying F508del/unknown represent about 3% (156 patients) of the overall Italian CF patients. In this paper we show that the Trikafta treatment of nasal epithelial cells, derived from F508del/Unknown patients, results in a significant rescue of CFTR activity. Based on our findings, we think that the F508del/Unknown patients considered in this study could obtain clinical benefits from Trikafta treatment, and we strongly suggest their eligibility for this type of treatment.This study, adding further evidence in the literature, once again confirms the validity of functional studies on nasal cells in the cystic fibrosis theratyping and personalized medicine.

Metabolic syndrome (MetS), first conceptualized in 1923, encompasses a cluster of metabolic abnormalities, including insulin resistance (IR), hyperglycemia, dyslipidemia, hypertension, and central obesity. This syndrome significantly increases the risk for cardiovascular disease (CVD) and type 2 diabetes mellitus (T2DM). Its global prevalence has surged, largely attributed to lifestyle factors such as poor diet and physical inactivity. Recent research highlights the crucial role of caveolae, small cellular structures, in the pathophysiology of MetS, particularly in insulin signaling, lipid metabolism, and vascular function. Managing MetS effectively requires a comprehensive strategy focused on lifestyle modifications and pharmacological treatments. Dietary approaches, environmental factors, regular physical activity and behavioral changes have demonstrated positive impacts on metabolic health. Despite current pharmacological options to individual conditions of MetS, emerging therapies targeting caveolae offer promising new avenues for treatment. While, microbiome modulation and certain natural agents may assist in managing the syndrome. Public health initiatives, including educational campaigns and stress reduction programs, are essential for the prevention and management of MetS. A multifaceted approach combining lifestyle changes, pharmacotherapy, and personalized interventions is vital for effective management. Future strategies may benefit from precision medicine and the integration of emerging biomarkers for targeted therapies.

Background: Cancer remains a leading cause of death worldwide. The progress in effective treatment has been hampered by challenges in personalized therapy, particularly in patients with comorbid conditions. The integration of artificial intelligence (AI) into patient profiling offers a promising approach to enhancing individualized anticancer therapy. Objective: This narrative review explores the role of AI in refining anticancer therapy through personalized profiling, with a specific focus on cancer patients with co-morbid migraine. Methods: A comprehensive literature search was conducted across multiple databases, including PubMed, Scopus, and Google Scholar. Studies were selected based on their relevance to AI applications in oncology and migraine management, with a focus on personalized medicine and predictive modeling. Key themes were synthesized to provide an overview of recent developments, challenges, and emerging directions. Results: AI technologies such as machine learning (ML), deep learning (DL), and natural language processing (NLP) have become instrumental in discovery of genetic and molecular biomarkers to cancer and migraine. These technologies also enable predictive analytics for assessing the impact of migraine on cancer therapy in co-morbid cases, predicting outcomes and provide clinical decision support systems (CDSS) for real-time treatment adjustments. Conclusion: AI holds significant potential to improve the precision and effectiveness of cancer management and therapy, particularly for cancer patients with co-morbid migraine. Nevertheless, challenges remain over data integration, clinical validation, and ethical consideration, which must be addressed to appreciate the full potential for outlines approach.

Despite medical advances, sepsis and septic shock remain one of the leading causes of mortality worldwide, with a high inter-individual variability in prognosis, clinical manifestations and response to treatment. Given the heterogeneity of sepsis and recent technological progress amongst machine learning analysis technique, a new, personalized approach in sepsis is being intensively studied. Despite the difficulties when tailoring a targeted approach, with the use of artificial intelligence-based pattern recognition, more and more publications are becoming available, highlighting novel factors that may intervene in the high heterogenicity of sepsis. This has led to the devise of a phenotypical approach in sepsis, further dividing patients based on host and trigger-related factors, clinical manifestations and progression towards organ deficiencies, dynamic prognosis algorithms and patient trajectory in the Intensive Care Unit (ICU). Host and trigger-related factors refer to patients’ comorbidities, body-mass index, age, temperature, immune response, type of bacteria and infection site. The progression to organ deficiencies refers to individual particularities of sepsis-related multiorgan failure. Finally, the patient’s trajectory in the ICU points out the need of a better understanding of interindividual response to various supportive therapies. This review aims to identify the main sources of variability in clustering septic patients in various clinical phenotypes, as a useful clinical tool for precision-based approach in sepsis and septic shock.

This study introduces the Data-Driven Personalized Learning Model (DDPLM), a sophisticated framework designed to enhance foreign language acquisition through the integration of big data analytics. Implemented within the educational platforms Edmodo and Duolingo, DDPLM utilizes real-time data processing to tailor learning paths and content dynamically to individual learner needs. Our findings indicate significant improvements in language learning efficiency, engagement, and retention. The model's adaptability across different digital environments showcases its potential scalability and effectiveness in various educational contexts. Additionally, the research addresses the critical role of personalized feedback and adaptive challenges in maintaining learner motivation and promoting deeper linguistic comprehension. The outcomes suggest that DDPLM significantly transforms traditional language education, making it more personalized, efficient, and aligned with individual learning preferences.

Vitamin D is crucial for athletes' health and performance. Although the exact vitamin D requirements for athletes have not been established, maintaining 25(OH)D levels of at least 40 ng/ml is considered beneficial. This study aimed to determine whether an individual loading dose formula for vitamin D supplementation is suitable to meet a target value of 40 ng/ml and is more effective than standardized supplementation. In a 10-week supplementation study during the winter months in Germany, 90 athletes with insufficient vitamin D levels (25(OH)D<30ng/ml) were randomly assigned to receive either a universal dose of 2000 IU/day of vitamin D or a loading dose of 4000 IU/day, followed by a maintenance dose of 1000 IU/day. Total 25(OH)D concentration was measured from dried blood spots at three time points: at baseline, at the computed date of 40ng/ml, and after the 10-week period. Additionally, a vitamin D-specific questionnaire was issued. On the day when 25(OH)D blood concentrations of 40 ng/ml were calculated to prevail, the individualized group had a significantly higher 25(OH)D level than the standardized group (41.1+-10.9 ng/ml vs. 32.5+-6.4 ng/ml, p<0.001). This study demonstrated that the examined formula is suitable for athletes to achieve a 25(OH)D concentration of 40 ng/ml. This indicates that a personalized approach is more effective than a one-size-fits-all approach in restoring adequate vitamin D levels in athletes.

Clopidogrel is one of the most prescribed antiplatelet drugs for cardiovascular diseases prevention. Clopidogrel is a prodrug metabolised by CYP2C19 in active metabolites with anti-aggregant effect. Pharmacogenetic variants such as CYP2C19*2 variant can alter the anti-aggregant effect of clopidogrel leading to a higher risk of cardiovascular disease relapse. The aim of this study was to evaluate the prescription characteristics of CYP2C19 pharmacogenetics in patients on clopidogrel treatment, based on a specific query in a clinical data warehouse. We evaluated the demographics of patients, prescribers, medical indications, CYP2C19 pharmacogenetic tests and therapeutic changes. Neurologists were the main prescribers of CYP2C19 pharmacogenetic tests associated with clopidogrel treatment in a context of suspicion of stroke relapse. Pharmacogenetic tests were mainly prescribed a posteriori to clopidogrel prescription. Clopidogrel pharmacogenetic resistance was associated with the prescription of lysine acetylsalicylate in substitution of clopidogrel. Clear clinical practice guidelines are needed to position CYP2C19 pharmacogenetic testing for patients on clopidogrel treatment.

In recent years, there has been an increasing demand for personalized experiences in various domains, including the cultural and educational sectors. Museums, as custodians of art, history, and scientific knowledge, are seeking innovative ways to engage their visitors and provide tailored content that enhances their understanding and appreciation of the exhibits. This article presents ExhibitXplorer, a distributed architecture service that leverages geofencing, artificial intelligence, and microservices to enable personalized content delivery in museums. By combining implicit and explicit segmentation of museum visitors and utilizing the GPT API for content generation, ExhibitXplorer offers a dynamic experience to different visitor segments, including researchers, students, casual visitors, and children. The system utilizes push notifications triggered by visitor location changes, allowing seamless delivery of personalized information both indoors and outdoors. Tests were conducted to evaluate the user experience of visitors to an outdoor ethnographic museum. The results showed that 55% of the test participants were satisfied and 45% very satisfied with the way personalized content was delivered.

Malaria remains a primary public health challenge in Africa, and there is growing interest in leveraging artificial intelligence (AI) to raise malaria interventions. This research examines the possible influence, challenges, and recommendations for implementing AI-based personalized malaria interventions in Africa. AI offers several opportunities in malaria management, including early detection and prediction of outbreaks, improved diagnosis, personalized interventions, optimal treatment recommendations, surveillance and response, resource optimization, and research innovation. However, the implementation of AI in malaria interventions faces various challenges. These include data availability and quality, infrastructure and resource constraints, contextual relevance and generalizability, ethical and privacy considerations, integration into healthcare workflows, and the need to build trust and acceptance among stakeholders. To address these challenges, it is recommended to strengthen data infrastructure, build local capacity in AI technologies, contextualize AI models to local settings, address ethical considerations, establish monitoring and evaluation frameworks, promote collaboration and knowledge sharing, and secure sustainable funding and long-term commitment. By considering these recommendations, stakeholders can work towards implementing AI-based personalized malaria interventions in Africa that can contribute to improving malaria control outcomes, reducing the weight of the disease, and advancing public health in the region.

The most aggressive primary malignant brain tumor in adults is glioblastoma (GBM), which has poor overall survival (OS). There is a high relapse rate among patients with GBM despite maxi-mally safe surgery, radiation therapy, temozolomide (TMZ), and aggressive treatment. Hence, there is an urgent and unmet clinical need for new approaches to managing GBM. The current study identified modules (MYC, EGFR, PIK3CA, SUZ12, and SPRK2) involved in GBM disease through the NeDRex plugin. Furthermore, hub genes were identified in a comprehensive interaction network containing 7,560 proteins related to GBM disease and 3,860 proteins associated with signaling pathways involved in GBM. By integrating the results of the aforementioned analyses and performing centrality analysis again, eleven key genes involved in GBM disease were identi-fied. ProteomicsDB or Gliovis databases were used for determining the gene expression in normal or tumor brain tissue. The NetworkAnalyst and the mGWAS-Explorer tools identified miRNAs, SNPs, and metabolites associated with these 11 genes. Moreover, a literature review of recent studies revealed other lists of metabolites related to GBM disease. The enrichment analysis of iden-tified genes, miRNAs, and metabolites associated with GBM disease was done using ExpressAna-lyst, miEAA, and MetaboAnalyst tools. Further investigation of metabolite roles in GBM was done through the pathway, joint pathway, and network analyses. The results of this study identified 11 genes (UBC, HDAC1, CTNNB1, TRIM28, CSNK2A1, RBBP4, TP53, APP, DAB1, PINK1, and RELN), five miRNAs (hsa-mir-221-3p, hsa-mir-30a-5p, hsa-mir-15a-5p, hsa-mir-130a-3p, hsa-let-7b-5p), six metabolites (HDL, N6-acetyl-L-lysine, cholesterol, formate, N, N-dimethylglycine/xylose and X2. piperidinone) and 15 distinct signaling pathways that play an indispensable role in the GBM disease development. To establish early diagnostic methods and plan personalized GBM treatment strategies, the identified top genes-miRNAs and metabolite signatures can be targeted.

Cognitive rehabilitation is useful for many after traumatic brain injury (TBI), but we lack critical knowledge about which patients benefit the most from different approaches. Advanced neuroimaging techniques have provided important insight into brain pathology and systems plasticity after TBI and have potential to inform new practices in cognitive rehabilitation. In this study, we aimed to identify candidate structural brain measures with relevance for rehabilitation of cognitive control (executive) function after TBI. Twenty-eight patients (9 female, mean age 40.5 (SD = 13.04) years) with moderate/severe TBI (>21 months since injury) that participated in a randomized controlled cognitive rehabilitation trial (NCT02692352) were included in the analyses. Regional brain volume was extracted from T1-weighted MRI scans before treatment using tensor-based morphometry. Both positive and negative associations between treatment outcome (everyday cognitive control function) and regional brain volume were observed. The most robust structural brain measures with relevance for improvement in function were observed in midline fronto-parietal regions, including the anterior and posterior cingulate cortices. The study pro-vides proof of concept and valuable insight for planning future studies focusing on neuroimaging in cognitive rehabilitation after TBI.

Chronic and non-healing wounds demand personalized and more effective therapies for treating complications and improve patient adherence. This work aims to develop a suitable chitosan-based scaffold to provide 24 hours controlled release of DKT, by taking advantage of chitosan’s thermo-responsive behavior as well as local hyperthermia in wounds. Three formulation prototypes were developed using chitosan (F1), 2:1 chitosan: PVA (F2), and 1:1 chitosan:gelatin (F3). Compatibility tests were done by DSC, TG, and IR spectroscopy. SEM was employed to examine the morphology of the surface and inner layers from the scaffolds. In vitro release studies were performed at 32 °C and 38 °C to evaluate the release profiles, which were later adjusted to different kinetic models for the best formulation. F3 showed the most controlled release of DKT at 32 °C for 24 hours (77.75 ± 2.72 %), and reduced the burst release in the initial 6 hours (40.18 ± 1.00 %), while at 38 °C the release reached 88.52 ± 2.07 % at 12 hours. The release profile for this formulation fits with Hixson-Crowell and Korsmeyer-Peppas kinetic models at both temperatures. Therefore, the developed chitosan/gelatin thermo-responsive scaffold provides a suitable system for wound healing with a controlled release of DKT for 24 hour-use, which can overcome adherence issues and wound complications.

Worldwide, over 2.2 million people are suffered from multiple sclerosis (MS), a multifactorial demyelinating disease of the central nervous system. MS is characterized by a wide range of motor, autonomic, and psychobehavioral symptoms including depression, anxiety, and dementia. The blood, cerebrospinal fluid, and postmortem brain samples of MS patients evidenced the disturbance of reduction-oxidation (redox) homeostasis such as the alterations of oxidative and antioxidative enzyme activities and the presence of degradation products. This review article discussed the components of redox homeostasis including reactive chemical species, oxidative enzymes, antioxidative enzymes, and degradation products. The reactive chemical species covered frequently discussed reactive oxygen/nitrogen species, infrequently featured reactive chemicals such as sulfur, carbonyl, halogen, selenium, and nucleophilic species that potentially act as reductive as well as pro-oxidative stressors. The antioxidative enzyme systems covered the nuclear factor erythroid-2-related factor 2 (NRF2)-Kelch-like ECH-associated protein 1 (KEAP1) signaling pathway. The NRF2 and other transcriptional factors potentially become a biomarker sensitive to the initial phase of oxidative stress. Altered components of the redox homeostasis in MS were discussed in search of a diagnostic, prognostic, predictive, and/or therapeutic biomarker. Finally, monitoring a battery of reactive chemical species, oxidative enzymes, antioxidative enzymes and degradation products helps evaluate the redox status of MS patients to expedite building personalized treatment plans for the sake of better quality of life.

In the context of food packaging design, customization enhances the value of a product by meeting the needs of the consumer. Personalization is also linked to adaptation. This makes it possible to improve the properties of the packaging from various points of view: functional, aesthetic, economic and ecological. Currently the functional and formal properties of packaging are not investigated among themselves, however the study of both properties are the basis for creating a new concept of personalized and sustainable product. In accordance with this approach, the conceptual design procedure of packaging with personalized and adapted geometries based on the digitization of fresh food is proposed in this work. This study is based on the application of advanced technologies for the design and development of food packaging, in this case apples, in order to improve the quality of the packaging. The results obtained show that it is possible to use advanced technologies in the early stages of product design in order to obtain competitive products adapted to new emerging needs.

Coordinated sets of extremely numerous digital data, on a given social or economic event, are treated by Artificial Intelligence tools to obtain reasonably accurate, valuable predictions. The same approach, applied to biomedical issues, as how to choose the right drug to completely cure a given cancer patient, does not reach satisfactory results. It is the “organized biological complexity”, which requires a different systems approach, to integrate, in an Augmented Intelligence strategy, statistical computations of digital data, network construction of “omics” findings, well-designed mathematical models and new experiments in an iterative pathway to reconstruct the “logic” beneath the “organized complexity”, as shown here for Systems Metabolomics of cancer. On this basis new diagnostic approaches, able to identify precision drug treatments, as well as new discovery strategy for more effective anti-cancer drugs are described.

Background/Objectives: Periodic fever, aphthous stomatitis, pharyngitis and cervical adenitis syndrome, often referred to as PFAPA syndrome, may enigmatically recur for an undetermined time in the affected children: a potential reason to explain its recurring pattern for an unpredictable period and its self-limitation is currently unknown. We explored the relationship of PFAPA symptom evolution during a variable follow-up with PFAPA disease pictures and breastfeeding for less than 6 months during infancy. Methods: In the Outpatients Clinic of Pediatric Rheumatology of our Institution we have retrospectively screened 150 Italian children with history of PFAPA syndrome during the period 2014-2024, all without any recognized chronic diseases: 88 males, 62 females, mean age at onset of 2.5±1.7 years, age range of 0.3-9.4 years, and mean age at diagnosis of 4.5±2.0 years. Results and Conclusions: After dividing patients into two groups based on either the disappearance or persistence of PFAPA symptoms during a variable median follow-up we found that positive family history of recurring fevers, cervical lymphadenopathy, arthralgia, myalgia, and breastfeeding for less than 6 months were associated with the persistence of the disease over time. Inherited autoinflammatory disorders were excluded on a genetic basis in 16.3% of the cohort. Performing a multivariate analysis adjusted for sex and age we found that only breastfeeding for more than 6 months and a higher level of education in PFAPA patients’ mothers were independently associated with the resolution of the recurring PFAPA symptoms.

This article provides a comprehensive overview of lung cancer from a genetic perspective, emphasizing the molecular pathways involved in its pathogenesis. This review presents the genetic syndromes and key genomic variations, including alterations and instabilities, that contribute to tumor development and progression. Furthermore, advancements in genomic testing, such as liquid biopsy, are explored, highlighting their utility in early diagnosis, prognostic evaluation, and personalized treatment strategies. The role of biomarkers in informing therapeutic decisions and optimizing patient outcomes is also examined, offering insights into their potential to refine clinical interventions. This review aims to enrich the understanding of the genetic landscape of lung cancer, providing insights that may lead to improved diagnostic and therapeutic approaches.

This paper explores the critical role of biomathematics in cancer research, focusing on how mathematical models enhance our understanding of tumor growth and optimize treatment strategies. It begins with an overview of cancer biology, highlighting the complexities of tumor dynamics and the molecular mechanisms driving growth. The paper then delves into various mathematical modeling approaches, including deterministic, stochastic, and agent-based models, illustrating their applications in predicting tumor behavior and treatment responses. Case studies demonstrate the real-world impact of these models on optimizing chemotherapy, radiation therapy, and immunotherapy. Challenges such as data availability, model complexity, and clinical translation are discussed, alongside future directions in the field, including advances in computational power and personalized medicine. Ultimately, this research emphasizes the transformative potential of biomathematics in improving cancer treatment outcomes and underscores the importance of interdisciplinary collaboration in advancing the field.

Cutaneous squamous cell carcinoma (cSCC) is the second most common form of skin cancer, with an increasing global incidence. While most cases are successfully treated with surgical excision, a subset can metastasize, leading to significant morbidity and mortality. Current staging systems based on clinical and histopathological features have shown limitations in accurately predicting metastatic risk. This review examines the role of gene expression profiling (GEP), particularly the 40-gene expression profile (40-GEP) test, in improving risk stratification and management of cSCC. We assess the prognostic value of the 40-GEP test, its integration with current staging systems, and its impact on clinical decision-making. Recent studies suggest that incorporating GEP results with traditional staging methods can enhance the identification of high-risk patients, potentially leading to more personalized treatment strategies. The review also explores the challenges of implementing GEP in routine clinical practice, including cost-effectiveness considerations and the need for standardization. Finally, we discuss the implications for future cSCC management and highlight areas for further research. As molecular profiling techniques continue to evolve, GEP represents a promising approach to optimizing care for cSCC patients, aligning with the growing emphasis on personalized medicine in oncology.

Background Homologous recombination deficiency (HRD) evolved into a major diagnostic marker in high grade ovarian cancer (HGOC) predicting the response to poly (Adenosine diphosphate-ribose) polymerase inhibitors (PARPi) and also platinum-based therapy. In addition to HRD, the type of peritoneal tumor spread influences treatment response and patient survival; miliary type tumor spread has poorer predicted outcome than non-miliary type tumor spread. Methods Known methods for HRD assessment were adapted for our technical requirements and the Predictive-value Integrated Genomic Instability Score (PIGIS) for HRD assessment evolved as an outcome. PIGIS was validated in HGOC samples from 122 patients. We used PIGIS to analyze whether the type of tumor spread correlated with HRD status and whether this had an impact on survival. Results We demonstrated that PIGIS can discriminate HRD positive from HRD negative samples. Tumors with miliary tumor spread are HRD negative and have a very bad prognosis with a progression free survival (PFS) of 15.6 months and an overall survival (OS) of 3.9 years. However, HRD negative non-miliary spreading tumors in our cohort had a much better prognosis (PFS 35.4 months, OS 8.9 years), similar to HRD positive tumors (PFS 34.7 months, OS 8.9 years). Conclusions Our results indicate that in a predominantly PARPi naïve cohort the type of tumor spread might be a better predictor of response to platinum sensitivity than HRD. It remains to be determined, whether this also applies for sensitivity to PARPi.

Background: Post-Covid condition can reduce activity and quality of life, resulting in a significant socioeconomic and health burden. Understanding its impact on patients' health is important for the development of personalized rehabilitation interventions. An independent association between obesity and post-covid condition was found because of complications and comorbidities. Methods: Sixteen patients with obesity and post-COVID symptoms (i.e., dyspnea, pain, poor sleep quality, muscle fatigue) admitted to the Istituto Auxologico Italiano, Piancavallo (VB), Italy, were recruited for a four-week rehabilitation program including conventional exercise therapy, nutritional intervention, psychological support and whole-body cryostimulation (WBC). Results: All participants attended all sessions of the program. Anthropometric data showed statistically significant changes in weight, waist circumference and body mass index. Biochemical analyses showed significant reductions in lipid and inflammatory profiles. There was a significant improvement in physical performance, reduction in pain and improvement in psychological well-being. Conclusion: A multidisciplinary rehabilitation protocol including WBC designed for patients with obesity and post-covid condition is safe and feasible. The overall improvements demonstrate that multidisciplinary rehabilitation was effective on post COVID patients and suggest that the use of WBC is safe and could play a role as a booster in rehabilitation programs.

Robin sequence is a congenital anomaly characterized by a triad of features that include micrognathia, glossoptosis, and airway obstruction. This comprehensive historical review maps the evolution of approaches and appliances for its treatment from the past until current modern possibilities of interdisciplinary combination of modern engineering, medicine, materials, and computer science combined approach with emphasis to design appliances inspired by nature - individual human anatomy. Current biomimetic designs are clinically applied resulting in appliances that are more efficient, comfortable, sustainable, and safer than legacy traditional designs. Review maps the treatment modalities that have been used for patients with Robin sequence over the years. Early management of Robin sequence focused primarily on airway maintenance and feeding support, while current management strategies involve both nonsurgical and surgical interventions and biomimetic biocompatible personalized appliances. The goal of this paper was to provide a review of the evolution of management strategies for patients with Robin sequence that led to the current interdisciplinary biomimetic approaches impacting the future of Robin Sequence treatment with biomimetics at the forefront.

Enophthalmos is a severe complication of primary reconstruction following orbital floor frac-tures, oncological resections or maxillo-facial syndromes. The goal of secondary reconstruction is to re-store symmetrical globe positions to recover function and aesthetics. In this article we pre-sent a method of computer-assisted orbital floor reconstruction using a mirroring technique and a customized titanium or high-density polyethylene mesh, printed using computer aided manufac-turing techniques. This reconstructive protocol involves 4 steps: mirroring of the healthy orbit at the affected site, vir-tual design of a patient specific orbital floor mesh, computer-assisted manufacturing (CAD) of the implant, and surgical insertion of the device. Using a computed tomography data set, the un-injured side of the craniofacial skeleton was reflected onto the contralateral injured side, and a reconstructive orbital floor prosthesis was virtually designed on the mirrored orbital bone sur-face. The solid-to-layer (.STL) files of the prosthesis were then manufactured using Direct Metal Laser Sintering (DMLS) or Computer Numerical Control (CNC) methods, leading to the produc-tion of 10 titanium meshes and 2 high density polyethylene (HDPE) implants. Clinical outcomes were assessed using 3dMD photogrammetry and computed tomography measures in 14 treated patients. The technique described here appears to be a viable method to correct complex orbital floor de-fects needing delayed reconstruction. This article represents the prosecution of a previous study, which is part of a wider experimental protocol for orbital floor reconstruction using comput-er-assisted design-computer-assisted manufacturing (CAD-CAM) technology

Breast cancer (BC) is the most common malignancy and the second cause of cancer specific death in women from high-income countries. More than 70% of all breast cancer are hormone receptor-positive BC, and elevated estrogen circulating in blood has been proved to be a strong risk for BC development. This is due to its contribution to enhance proliferation of cancerous cells, angiogenesis and metastasis stimulation and therapy resistance. The estrogen (E) metabolism–gut microbiome axis is functional with underlying individual variations in E levels. It is reasonable that the estrobolome (bacterial genes whose products are capable of metabolizing E) could contribute to the risk of hormone-driven malignancies including BC and may serve as a potential biomarker and target. Gut microbial β -glucuronidase (GUS) enzymes have been suggested to be involved in the estrobolome. Furthermore, bacterial GUS enzymes within the gastrointestinal tract have been postulated to contribute to hormone breast cancer. In this review, we discuss the recent knowledge about the role of GUS enzyme in the pathogenesis of breast cancer. We focus on the role of GUS in (i)-the microbioma and estrogen metabolism, (ii) diet, estrobolome, and the BC development, (iii) other activities of the bacterial β-glucuronidase, and (iv) the new molecular target for BC therapeutic application.

Blood glucose (BG) monitoring is an important issue for critically ill patients. Previous studies reported that poor sugar control was associated with increased mortality in admitted patients. However, repeated blood glucose monitoring can be resource-consuming and cause a healthcare burden in clinical practice. In this study, we aimed to develop a personalized machine-learning model to predict dysglycemia based on electrocardiogram (ECG) findings. The study included patients with more than 20 ECG records during single hospital admission in the Medical Information Mart for Intensive Care III database, focusing on the lead II recordings, along with the corresponding blood sugar data. We processed the data and used ECG features from each heartbeat as inputs to develop a one-class support vector machine (SVM) algorithm to predict dysglycemia. The model prediction for dysglycemia using a single heartbeat had an AUC level of 0.92 ± 0.09, with a sensitivity of 0.92 ± 0.10 and specificity of 0.84 ± 0.04. Based on 10 s majority voting, the model prediction for dysglycemia improved to an AUC of 0.97 ± 0.06. In this study, we found that a personalized machine-learning algorithm could accurately detect dysglycemia using a single-lead ECG.

Aim of this research paper is to introduce a novel method of hybrid orthodontic tooth-borne distalizer treatment of class II malocclusion by using 3D printed biocompatible personalized distalizer. Explains 3D designing, printing and clinical application of individualized biocompatible medical device dedicated for orthodontic teeth distalization. Compares such distalizer manufactured from two different biocompatible photopolymers (white and transparent). Evaluates their clinical performance and also patients’ aesthetical perception. Clinical part includes comparison of treatment debonding on the set of 12 complete orthodontic treatments with uni-lateral class II malocclusion managed with hybrid approach (CAT-Invisalign with 3D printed distalizer). Paper offers an evaluation of the personalized distalizer functioning in regard to current publications and comparison to conventional prefabricated alternatives like Carriere® Distalizer™ appliance. Results showed no significance of material differences on clinical performance of such individualized distalizers. Research showed preference of patients towards transparent biocompatible photopolymer instead of white A2 shade.

Plants and plant-derived food products have been used for medicinal purposes since the ancient. Medicinal Plant-based functional foods or plant-based dietary compounds are a re-emerged interest for their therapeutic benefits and nutritive supports which has implicated in healthcare systems across the world. Neurological disorders are one of the greatest threats to public health and according to the World Health Organization, about 100 million people are affected globally by several neurological and mental ailments. In a traditional medication system, medicinal plants have been applied as both neuro-therapeutic purposes and micro-macro nutrients provider for the wellbeing of psychological states e.g. anti-depressant, anti-anxiety, anti-convulsions, anti-dementia, anti-psychotic, etc. Herein, it is a topic of great interest to present a conceptual aspect by reviewing relevant scientific literature about the plant-based functional foods or bioactive phytochemicals for the prevention and treatment of mental and neurological disorders. From the literature assessment, we have found that nutritional neuroscience is becoming an advanced research discipline and there has been a growing pile of evidence concerning the therapeutic use of plant-based functional foods and/or plant-derived food compounds for the management of neurologic health, evolving with promising impact over the time.

Liver disease can significantly impact life expectancy, making early diagnosis and therapeutic intervention critical challenges in medical care. Imaging diagnostics play a crucial role in diagnosing and managing liver diseases. Recently, the application of artificial intelligence (AI) in medical imaging analysis has become indispensable for healthcare. AI, trained on vast datasets of medical images, has sometimes demonstrated diagnostic accuracy that surpasses human experts. AI-assisted imaging diagnostics are expected to contribute significantly to the standardization of diagnostic quality. Furthermore, AI has the potential to identify image features imperceptible to humans, thereby playing an essential role in clinical decision-making. This capability enables physicians to formulate more accurate diagnoses and develop effective treatment strategies, ultimately improving patient outcomes. Additionally, AI is anticipated to become a powerful tool in personalized medicine. By integrating individual patient imaging data with clinical information, AI can propose optimal treatment plans, making it an essential component in providing the most appropriate care for each patient. Current reports highlight the advantages of AI in managing liver diseases. As AI technology continues to evolve, it is expected to further advance personalized diagnostics and treatments, contributing to overall improvements in healthcare quality.

Type 2 Diabetes (T2D) is a major public health concern. We have previously shown that the biochemical functions of the gut microbiome contribute to the postprandial glucose response and built a machine learned (ML) model that can personalize dietary choices using a stool metatranscriptomic test. We integrated this data-driven personalized nutrition process into the Viome Precision Nutrition Program (VPNP), which includes sample collection kits (capillary blood and stool), an AI/ML nutritional recommendation engine, and an app for digital delivery of dietary recommendations. To test the efficacy of VPNP, we conducted a decentralized, blinded, randomized, placebo-controlled trial in a prediabetic and diabetic adult population in the USA, with HbA1c as the primary endpoint. For this pilot trial, we recruited 27 participants and randomized them into two arms. The placebo arm included sample collection, digital delivery of the USDA dietary recommendations, and physical delivery of supplements filled with inert material. The VPNP (interventional) arm included digital delivery of data-driven and personalized dietary recommendations, and physical delivery of precision supplements. The nutritional intervention lasted ~90 days. The ANCOVA analysis method was employed to control for potential confounders measured at baseline. VPNP intervention (n=12) reduced HbA1c more than the placebo (n=15) in a clinically significant manner (HbA1c difference of 0.42%, p = 0.028). We demonstrate that a precision nutrition program based on stool and blood metatranscriptomic data and AI/ML analyses can significantly reduce HbA1c relative to USDA recommended nutrition in a prediabetic and diabetic population. These results suggest that integrating data-driven precision nutrition into the healthcare system could reduce the burden of T2D.

This paper presents a novel framework for Collaborative Learning Networks (CLNs) in classrooms, aiming to optimize resource utilization and enhance educational services. The study addresses the inefficiencies of traditional education models, particularly the underutilization of computational resources and limited access to specialized software. The proposed framework employs a mix of task-based and random allocation strategies to distribute computational tasks within the CLN, considering each device’s capabilities and task requirements. The findings show significant improvement in resource utilization, facilitated by device-to-device collaboration and task sharing, thus enriching students’ learning experiences. This work contributes to optimizing resource utilization in education, advancing the field of CLNs.

In an era where dietary habits significantly impact health, technological interventions can offer personalized and accessible food choices. This paper introduces Chef Dalle, a recipe recommen-dation system that leverages multimodal human-computer interaction (HCI) techniques to pro-vide personalized cooking guidance. The application integrates voice-to-text conversion via Whisper, ingredient image recognition through GPT-Vision, and employs TF-IDF vectorization alongside cosine similarity for personalized recipe recommendations. These methods enable users to interact with the system using voice, text, or images, accommodating various dietary re-strictions and preferences. Furthermore, the utilization of DALL-E 3 for generating recipe images enhances user engagement. User feedback mechanisms allow for the refinement of future rec-ommendations, demonstrating the system's adaptability. Chef Dalle showcases potential appli-cations ranging from home kitchens to grocery stores and restaurant menu customization, ad-dresses accessibility, promoting healthier eating habits. This paper underscores the significance of multimodal HCI in enhancing culinary experiences, setting a precedent for future developments in the field.

This study addresses the challenge of treating skin cancer effectively, especially in visible areas like the face, where traditional methods may be undesirable. Current options, including surgery and radiation therapy, have limitations such as complexity, expense, and imprecise dose delivery. The proposed solution involves additively manufacturing personalized patches using diagnostic imaging data, offering a targeted and controlled dose of beta-emitting isotopes like Y-90. The patches, tailored to the tumor's shape and depth, aim to minimize damage to healthy tissues and structures. Through 3D printing technology we demonstrated the feasibility of creating individualized applicators filled with radioisotopes, that have the potential to deliver therapeutic doses while ensuring patient comfort and affordability. This innovative approach, based on radiation delivery simulations, offers a promising future alternative to conventional radiation therapy methods. Clinical trials are required to validate its efficacy and safety, but the potential for reduced radiation exposure and enhanced treatment precision positions this personalized and targeted therapy as a valuable advancement in skin cancer treatment.

This study introduces the Dynamic Feedback-Driven Learning Optimization Framework (DFDLOF), an innovative machine learning-based approach to personalize educational pathways. DFDLOF leverages advanced algorithms for in-depth analysis of learner data, enabling the dynamic customization of educational content and methodologies. Its application in diverse online platforms, including Khan Academy and Coursera, underscores its adaptability and scalability. DFDLOF enhances learner engagement and outcomes by providing tailored learning experiences and real-time feedback. Key insights reveal its strengths in personalized education, particularly in dynamically adapting learning paths and the importance of real-time feedback. Challenges identified include integration complexities and data privacy concerns. Future research directions emphasize the need for longitudinal impact studies and broader demographic applications. This study contributes significantly to the evolving domain of adaptive learning technologies, demonstrating the transformative potential of machine learning in personalizing education.

Patient-derived tumor organoids (PDTOs) are a novel preclinical model of non-small cell lung cancer (NSCLC) for studying tumor biology and precision medicine, which recapitulates tumor morphology and gene expression profile. However, some issues such as low establishment rates, long-term production, and the loss of the immune microenvironment have hindered their widespread practical use. In this study, we investigated the efficacy of a recently introduced free-floating organoid production method to address these issues. The free-floating approach provided PDTO establishment success rate of over 90 % resulting in organoid formation within a short period of one week. These organoids retained the elements of the parental tumor morphology and contained stroma and immune cell populations, as confirmed by flow cytometry analysis. Importantly, we found that the cytokine and growth factor expression profiles of the free-floating organoids correlated with those of the original tumors in 58 % of cases, indicating the preservation of tumor-associated signaling pathways. Obtained organoids exhibited different responses to anticancer chemotherapeutics that might be a result of variable expression of drug resistance-associated genes. Considering the high establishment rate and short time of free-floating organoid production, this technique provides a valuable tool for a more accurate evaluation of therapeutic strategies and cancer biology studies.

Despite evidence-based therapies, patients presenting with atherosclerosis involving more than one vascular bed, such as those with peripheral artery disease (PAD) and concomitant coronary artery disease (CAD), constitute a particular vulnerable group characterized by enhanced residual long-term risk for major adverse cardiac events (MACE), as well as major adverse limb events (MALE). The latter are progressively emerging as a hard outcome to be targeted, being correlated with increased mortality. Antithrombotic therapy is the mainstay of secondary prevention in both patients with PAD or CAD however, the optimal intensity of such therapy is still topic of debate, particularly in the post-acute and long-term setting. Recent well powered randomized clinical trials (RCTs) have provided data in favor of a more intense antithrombotic therapy, such as prolonged dual antiplatelet therapy (DAPT) with aspirin and a P2Y12 inhibitor or a therapy with aspirin combined with an anticoagulant drug. Both approaches increase bleeding and patients selection is a key issue. The aim of this review is therefore to discuss and summarize the most updated available evidences for different strategies of anti-thrombotic therapies in patients with chronic PAD and CAD, particularly focusing on studies enrolling patients with both type of atherosclerotic disease and comparing a higher versus a lower intensity antithrombotic strategy. The final objective is to identify the optimal tailored approach in this setting, to achieve the greatest cardiovascular benefit and improving precision medicine.

The use of artificial intelligence (AI) applications in education has the potential to revolutionize the learning experience for international students, who face unique challenges when studying in a foreign country. This paper explores various examples of AI applications in education and their potential impact on international students. AI applications such as personalized learning experiences, adaptive testing, predictive analytics, and chatbots for learning and research are examined for their potential to improve learning efficiency and provide customized education support. It also explores the significant risks and limitations associated with AI technologies, such as privacy, cultural differences, language proficiency, and ethical implications. To maximize the potential benefits of AI applications in higher education, it is crucial to implement appropriate safeguards and regulations. This paper provides a starting point for research on the potential impact of artificial intelligence on international students’ educational experiences and how AI may be integrated into educational administration and learning processes.

Nutraceuticals have taken the spotlight during the past two decades as evidenced by the exponential publications on them. Long a part of routine in Traditional Medicine Systems, the rise of their mainstream use globally raises both safety concerns and need for better understanding of efficacious dosing. We attempt to answer these questions in this preliminary scoping review by an analysis of current literature on nutraceutical use as a personalized or prescription medicine. Using Covidence, Rayyan, and manual searches of PubMed, 598 unique publications were selected. 32 are systematic reviews, of which we overview the scope. We also overview 30 papers that address adverse drug reactions. To obtain an unbiased landscape of the 598 papers, we analyzed keywords using multiple methods. Expectedly, the most frequent keywords were probiotics and vitamins. Unexpectedly and remarkably, among the highest keyword yield was ‘COVID’. Further exploring this aspect, we review 15 pertinent papers, that not only provide robust evidence for nutraceutical benefits as part of SARS-CoV-2 treatment, but also amplify the notion that nutraceuticals are protective. Overall, the strident note is that further robust targeted research is needed in order to reap the full benefits of nutraceuticals in a safe and efficacious manner.

Customized wrist splints, particularly for upper extremity orthoses like wrist support braces, are commonly used across numerous clinical scenarios. However, the traditional process for producing personalized wrist splints is largely manual and highly dependent on the expertise of orthopedic specialists. This experience-based approach often leads to suboptimal outcomes, necessitating further refinement of the designs. Recent advancements in Additive Manufacturing (AM) have brought significant innovation to various industries, including orthopedics. This study aims to present a comprehensive methodology that integrates advanced design tools ,like 3D Scanning, with digital manufacturing techniques to produce tailored wrist splints. The produced hand brace aims to offer enhanced mechanical performance and comfort by precisely fitting an individual’s anatomy while minimizing material usage and weight. To achieve optimal design efficiency, the study explores the application of a Topology Optimization (TO) approach for design, while the manufacturing process utilizes Fused Deposition Modeling (FDM), an evolving technology within the Additive Manufacturing (AM) sector.

This paper discusses the application of artificial intelligence in imaging omics, especially in cancer research. Imaging omics enables detailed analysis of spatial and temporal heterogeneity of tumours through high-throughput extraction of quantitative features from medical images such as MRI, PET, and CT. This paper focuses on applying PARKS systems to automate the recognition, segmentation, and extraction of image features, significantly enhancing the capabilities of clinical decision support systems (CDSS). The future direction is to establish a robust network infrastructure for radiology Medication-led Health care (RLHC) to facilitate the development and application of personalised treatment protocols, and to improve diagnostic accuracy, prognosis assessment, and treatment recommendations by uploading quantitative image features to a shared database and comparing them with historical images.

Betel quid (BQ) use disorder (BUD) is prevalent in many Asian countries, impacting approximately 600 million people. We conducted a randomized clinical trial to analyze the impact of MAOA genetic variations on the severity of BQ craving. This was measured using DSM-5 criteria and Yale-Brown Obsessive Compulsive Scale modified for betel-quid (Y-BOCS-BQ). Participants were grouped according to the severity of BUD and MAOA gene single nucleotide polymorphism (SNP) rs5953210 genotypes. Y-BOCS-BQ were assessed at baseline (week 0) and during follow-up at weeks 2, 4, 6, and 8. The AA genotype group showed significantly greater reductions in Y-BOCS-BQ at weeks 2 (p=0.0194), 4 (p=0.0078), 6 (p=0.0277), and 8 (p=0.0376) compared to the GG genotype group. Additionally, within the antidepressant group, the AA genotype showed significant reductions in Y-BOCS-BQ at weeks 2 (p=0.0313), 4 (p=0.0134), 6 (p=0.0061), and 8 (p=0.0241) compared to the GG genotype. Statistical analysis revealed a significant interaction between the treatment and placebo groups based on MAOA genotypes, with the AA genotype in the treatment group exhibiting a more pronounced decrease in Y-BOCS-BQ (p interaction <0.05) at week 6. These findings highlight the importance of considering genetic factors when developing personalized treatment plans for BUD.

Frailty is a common condition in older adults that negatively impacts health and quality of life. This study evaluated a comprehensive, personalized, and coordinated intervention under the Value-Based Care approach to address frailty's multidimensional nature in older people. It em-ployed a pre-post randomized controlled design, involving frail individuals over 65 years in Valencia, Spain. Assessments were conducted at baseline, 12 months (immediately post-intervention), and 18 months. The intervention included a personalized care plan supported by technology, with monthly motivational follow-ups and plan updates by health professionals and participants. Outcomes were measured using an assessment questionnaire that included the International Consortium for Health Outcomes Measurement dataset for the older population: physical health, physical functioning, general mental health, satisfaction with social activities and relationships, ability to carry out usual social roles and activities, pain, general quality of life, loneliness, physical frailty, psychological frailty and social frailty. The study found signifi-cant improvements in physical frailty, quality of life, reduced health resource use and hospitali-zations, and lower levels of pain and depression/anxiety compared to baseline. The findings suggest further research into value-based care approaches, emphasizing the development and activation of personalized, comprehensive programs for older individuals with frailty.

Same-day desensitization (SDD) is a procedure in which desensitization to the drug suspected of causing the hypersensitivity reaction (HR) is performed on the same day as the patient's reaction. In rapid drug desensitization (RDD), desensitization to the suspected drug is performed after an allergological study. The aim of the study was to analyze the diagnostic capacity of the allergist in Oncology Day Hospital (ODH) to establish the phenotype of the HR, in the context of SDD, based exclusively on clinical biomarkers. This is a study of patients treated with desensitization on the same day they had a HR. Later an allergological study (clinical history, skin test, risk assessment, serological biomarkers, drug provocation test if applicable) was performed prior to the following administrations. A total of 35 successful SDDs were performed. A high concordance (91.5% of patients) was found between the phenotype suspected by the allergist after the initial assessment of the patient prior to SDD and the final phenotype after subsequent allergological study performed in the Allergy Department. The presence of the allergist at the ODH allows, in addition to initial treatment according to phenotype, desensitization on the day of the reaction. This will lead to personalized and precision allergology.

Diabetic foot ulcers (DFUs) significantly affect the lives of patients and increase the risk of hospital stay and amputation. We suggest a remote-monitoring platform for better DFU care. This system uses digital health metrics (scaled from 0 to 10, where higher scores indicate a greater risk of slow healing) to provide a comprehensive overview through a visual interface. The platform features smart offloading devices that capture behavioral metrics such as offloading adherence, daily steps, and cadence. Coupled with remotely measurable frailty and phenotypic metrics, it offers an in-depth patient profile. Additional demographic data, wound characteristics, and clinical parameters, such as cognitive function, were integrated, contributing to a comprehensive risk factor profile. We evaluated the feasibility of this platform with 124 DFU patients over 12 weeks; 39% experienced unfavorable outcomes such as dropout, adverse events, or non-healing. Digital biomarkers were benchmarked (0-10), categorized into low, medium, and high risk for unfavorable outcomes, and visually represented using color-coded radar plots. The initial results of case reports illustrate the value of this holistic visualization to pinpoint the underlying risk factors for unfavorable outcomes, including a high number of steps, poor adherence, and cognitive impairment. Although future studies are needed to validate the effectiveness of this visualization in personalizing care and improving wound outcomes, early results in identifying risk factors for unfavorable outcomes are promising.

Fused deposition modeling (FDM) is a rather new technology in the production of personalized dosage forms. Melting and printing of polymer–active pharmaceutical ingredient (API) – mixtures can be used to produce oral dosage forms with different dosage as well as release behavior. This process is utilized to increase the bioavailability of pharmaceutically relevant active ingredients that are poorly soluble in physiological medium by transforming them into solid amorphous dispersions (ASD). Release from such ASDs is expected to be faster and higher compared to the raw materials and thus enhance bioavailability. Printing directly from powder while forming ASDs was realized. Different techniques to change release patterns as well as a non-destructive way for determination of API distribution were shown. By measuring the melt viscosities of mixtures printed a rheological model for the printer used is proposed.

Circulating tumor cells (CTCs) are cells released from the primary tumor and intravasate into the blood or lymphatic vessels, where they are transported to distant sites and act as seeds that initiate cancer metastases. Recent advances in CTC research have shown their relevance as prognostic markers for early and metastatic disease detection, predictive biomarkers for relapse, and response to medical intervention or therapy. The rapidly evolving landscape of CTC biology has opened new avenues for understanding cancer progression, metastasis, and treatment response. Additionally, translating these findings into clinical applications holds promise for improving cancer diagnostics, prognosis, and personalized therapeutic strategies. This review discusses the significance of CTCs in cancer research and their associated challenges. We explore recent developments in the detection and characterization of CTCs and their implications in cancer research and clinical practice.