Personal Approach Research Articles

Personalized oncology in pheochromocytomas and paragangliomas: integrating genetic analysis with machine learning.

Pheochromocytomas and paragangliomas (PCCs/PGLs) are uncommon neuroendocrine tumors with a significant genetic tendency. Approximately 35-40% of these tumors are associated with genetic factors. The present study performed a thorough analysis using publicly accessible genetic and clinical data from the Cancer Genome Atlas (TCGA) to examine the involvement of six genes, namely GBP1, KIF13B, GPT, CSDE1, CEP164, and CLCA1, in the development of PCCs/PGLs. By employing multi-omics data, this study investigates the relationship between mutational patterns and the prognosis of tumors, focusing on the possibility of tailoring treatment methods to individual patients. The study utilizes Mutect2 to detect somatic mutations with high confidence in whole-exome sequencing data from PCCG samples. The study uncovers mild effects on protein function caused by particular mutations, including GBP1 (p.Cys12Tyr), KIF13B (p.Arg847Gly), and GPT (p.Gln50Arg). A random forest classifier uses mutational profiles to predict potential drug recommendations, proposing a focused therapy strategy. This study thoroughly analyzes the genetic mutations found in PCCs/PGLs, highlighting the significance of precision medicine in developing specific treatments for these uncommon types of cancer. This study aims to improve the understanding of the development of tumors and identify personalized treatment approaches by combining genetic data with machine learning analyses.

Brain and Muscle derived features to discriminate simple hand motor tasks for a rehabilitative BCI: comparative study on healthy and post-stroke individuals.

Brain-Computer Interfaces targeting post-stroke recovery of the upper limb employ mainly electroencephalography to decode movement-related brain activation. Recently hybrid systems including muscular activity were introduced. We compared the motor task discrimination abilities of three different features, namely event-related desynchronization/synchronization (ERD/ERS) and movement-related cortical potential (MRCP) as brain-derived features and cortico-muscular coherence (CMC) as a hybrid brain-muscle derived feature, elicited in 13 healthy subjects and 13 stroke patients during the execution/attempt of two simple hand motor tasks (finger extension and grasping) commonly employed in upper limb rehabilitation protocols. 
Approach. We employed a three-way statistical design to investigate whether their ability to discriminate the two movements follows a specific temporal evolution along the movement execution and is eventually different among the three features and between the two groups. We also investigated the differences in performance at the single-subject level.
Main results. The ERD/ERS and the CMC-based classification showed similar temporal evolutions of the performance with a significant increase in accuracy during the execution phase while MRCP-based accuracy peaked at movement onset. Such temporal dynamics were similar but slower in stroke patients when the movements were attempted with the affected hand. Moreover, CMC outperformed the two brain features in healthy subjects and stroke patients when performing the task with their unaffected hand, whereas a higher variability across subjects was observed in patients performing the tasks with their affected hand. Interestingly, brain features performed better in this latter condition with respect to healthy subjects. 
Significance. Our results provide hints to improve the design of Brain-Computer Interfaces for post-stroke rehabilitation, emphasizing the need for personalized approaches tailored to patients' characteristics and to the intended rehabilitative target.

AI fusion of multisource data identifies key features of vitiligo

Vitiligo is a skin disorder that is associated with a decreased risk of skin cancer, but it can lead to increased susceptibility to sunburn, psychological distress, and disruptions in daily life, consists of two primary subtypes: segmental and nonsegmental vitiligo, each with distinct underlying mechanisms. However, the reliable identification of diagnostic markers and the ability to differentiate between these subtypes have remained elusive challenges. This study aims to pioneer predictive algorithms for vitiligo diagnosis, harnessing the capabilities of AI (Artificial Intelligence) to amalgamate multisource data and uncover essential features for distinguishing vitiligo subtypes.An ensemble algorithm was thoughtfully developed for vitiligo diagnosis, utilizing a spectrum of machine learning techniques to evaluate the likelihood of vitiligo, whether segmental or nonsegmental. Diverse machine learning methodologies were applied to distinguish between healthy individuals and vitiligo patients, as well as to differentiate segmental from nonsegmental vitiligo. The ensemble algorithm achieved a remarkable AUC (Area Under the Curve) of 0.99 and an accuracy of 0.98 for diagnosing vitiligo. Furthermore, in predicting the development of segmental or nonsegmental vitiligo, the model exhibited an AUC of 0.79 and an accuracy of 0.73. Key parameters for vitiligo identification encompassed factors such as age, FBC (full blood count)-neutrophils, FBC-lymphocytes, LKF(liver and kidney function)-direct bilirubin, LKF-total bilirubin, and LKF-total protein levels. In contrast, vital indicators for monitoring the progression of segmental and nonsegmental vitiligo included FBC-B lymphocyte count, FBC-NK (Natural Killer) cell count, and LKF-alkaline phosphatase levels. This retrospective study underscores the potential of AI-driven analysis in identifying significant risk factors for vitiligo and predicting its subtypes at an early stage. These findings offer great promise for the development of effective diagnostic tools and the implementation of personalized treatment approaches in managing this challenging skin disorder.

Exploring the impact of stimulant medications on weight in children with attention deficit hyperactivity disorder in Dubai, United Arab Emirates.

Attention deficit hyperactivity disorder (ADHD), prevalent in 5% of children worldwide, impacts academic performance and often coexists with psychiatric disorders. Psychostimulant medications are primary treatments for ADHD, enhancing dopamine to reduce symptoms. However, dopamine increase may cause appetite loss. This pioneering study in the United Arab Emirates (UAE) explores psychostimulant effects on weight in children diagnosed with ADHD, aiming to uncover unique regional characteristics and contributing factors to weight changes. This retrospective cohort study assessed data from electronic medical records from 2017 to 2022, aiming to assess the impact of psychostimulants on weight in children aged 6-18 years. Inclusion criteria covered psychostimulant-treated and untreated patients with ADHD. Statistical analysis, involving longitudinal data methods aimed to demonstrate significant weight differences. Data from 107 pediatric patients diagnosed with ADHD were analyzed, with 86 meeting inclusion criteria. Most patients were male (80.2%). ADHD presentations varied, and methylphenidate immediate release was the most prescribed stimulant medication. Patients experienced initial weight loss followed by overall gain over 12 months. Coexisting conditions, maternal factors, family history, and correlations with autism spectrum disorder were explored. This study provides valuable insights into the effects of psychostimulant medications on the weight of children and adolescents diagnosed with ADHD in the UAE. It suggests avenues for future research, emphasizing extended follow-ups to understand long-term psychostimulant effects, nuanced examinations of age and gender, and exploring interactions with comorbidities. Despite limitations, the research provides insights into ADHD medication effects, guiding personalized treatment approaches for pediatric populations.

Impact of contrast-enhanced CT in the dosimetry of SBRT for liver metastases treated with MR-Linac

BackgroundTo investigate the impact of using contrast-enhanced computed tomography (CHCT) in the dosimetry of stereotactic body radiation therapy (SBRT) for liver metastases treated with MR-Linac.MethodsA retrospective study was conducted on 21 liver cancer patients treated with SBRT (50 Gy in 5 fractions) using a 1.5 Tesla Unity MR-Linac. The clinical treatment plans optimised on plain computed tomography (pCT) were used as reference. The electronic density (ED) of regions of interest (ROIs) including the liver, duodenum, esophagus, spinal cord, heart, ribs, and lungs, from pCT and CHCT, was analysed. The average ED of each ROI from CHCT was used to generate synthetic CT (sCT) images by assigning the average ED value from the CHCT to the pCT. Clinical plans were recalculated on sCT images. Dosimetric comparisons between the original treatment plan (TPpCT) and the sCT plan (TPsCT) were performed using dose-volume histogram (DVH) parameters, and gamma analysis.ResultsSignificant ED differences (p < 0.05) were observed in the liver, great vessels, heart, lungs, and spinal cord between CHCT and pCT, with the lungs showing the largest differences (average deviation of 11.73% and 12.15% for the left and right lung, respectively). The target volume covered by the prescribed dose (VDpre), and the dose received by 2% and 98% of the volume (D2%, and D98%, respectively) showed statistical differences (p < 0.05), while the gradient index (GI) and the conformity index (CI) did not. Average deviations in target volume dosimetric parameters were below 1.02%, with a maximum deviation of 5.57% for. For the organs at risk (OARs), significant differences (p < 0.05) were observed for D0.35cc and D1.2cc of the spinal cord, D10cc for the stomach, D0.5cc for the heart, and D30% for the liver-GTV, with mean deviations lower than 1.83% for all the above OARs. Gamma analysis using 2%-2 mm criteria yielded a median value of 95.64% (range 82.22–99.65%) for the target volume and 99.40% (range 58–100%) for the OARs.ConclusionThe findings suggest that the use of CHCT in the SBRT workflow for liver metastases may result in minor target volume overdosage, indicating its potential for adoption in clinical settings. However, its use should be further explored in a broader context and tied to personalized treatment approaches.

Advances in microRNAs as Emerging Biomarkers for Colorectal Cancer Early Detection and Diagnosis.

Colorectal cancer (CRC) remains the second most common cause of cancer-related mortality worldwide, necessitating advancements in early detection and innovative treatment strategies. MicroRNAs (miRNAs), small non-coding RNAs involved in gene regulation, have emerged as crucial players in the pathogenesis of CRC. This review synthesizes the latest findings on miRNA deregulated in precancerous lesions and in CRC. By examining the deregulation patterns of miRNAs across different stages of CRC development, this review highlights their potential as diagnostic tools. We specifically analyse the roles and diagnostic relevance of four miRNAs-miR-15b, miR-21, miR-31, and miR-146a-that consistently exhibit altered expression in CRC. The current knowledge of their role in key oncogenic pathways, drug resistance, and clinical relevance is discussed. Despite challenges posed by the heterogeneity of the research findings on miRNA deregulation and their role in CRC, integrating miRNA diagnostics into current screening methods holds promise for enhancing personalized medicine approaches. This review emphasizes the transformative potential of miRNAs in CRC diagnosis, paving the way for improved patient outcomes and novel therapeutic paradigms.

Quo Vadis, Elimination Voltammetry?

The article presents an open personal approach to the future of elimination voltammetry with linear scan (EVLS) in the context of the expected development and research of new materials and new technologies. With this development, the application capability of EVLS will be expanded, the basis of which is a mathematical apparatus that allows some current components to be eliminated from the overall voltammetric record, while others to be preserved. Although EVLS played a provisional and unquestionable role in electroanalytical applications, where it helped to obtain lower limits of detection (LOD) values of various organic and inorganic substances and to reveal electrode processes hidden in voltammetric signals, its further development indicates promising use in other research areas as well. The aim of the communication is a more precise but also more general definition of the elimination method, its specific paradigms, prospects and goals for the future.

A randomised controlled trial to determine the effect of genotype-based personalised diet and physical activity advice for FTO genotype (rs9939609) delivered via email on healthy eating motivation in young adults.

The prevalence of obesity continues to rise, and public health dietary recommendations are not being adhered to. The transition to higher education is a period of risk for weight gain in young adults and has been demonstrated as a good time to initiate behaviour change. A genotype-based personalised approach to dietary recommendations may motivate young adults to maintain or adopt positive dietary behaviours. The aim of the present study was to determine the efficacy of genotype-based personalised dietary and physical activity advice on healthy eating motivation in young adults. Participants were young adults (n = 153), aged 18-25 years. Baseline measures (participant characteristics, height, weight, body mass index [BMI], body fat percentage [BF%], healthy eating motivation and physical activity) were collected. Participants were genotyped for a SNP in the FTO gene (rs99396090) and randomly allocated (stratified for genotype) to three different groups (1. Genotype-based personalised advice: dietary and physical activity advice based on genotype, BMI and reported physical activity; 2. Non-genotype-based personalised advice: dietary and physical activity advice based on BMI and reported physical activity; 3. Control: no advice). A week after receipt of advice delivered via email, participants completed the healthy eating motivation questionnaire for a second time. Genotype-based personalised dietary advice did not affect healthy eating motivation: when participants were analysed across the whole group (p = 0.417), when analysed according to those informed of a risk or non-risk-associated genotype (p = 0.287), or when analysed according to those with a BMI (>25 kg/m2; p = 0.336) or BF% (male >18%, female >31%; p = 0.387) outside the healthy range. There was also no significant difference in healthy eating motivation at 1-week in the control or non-genotype-based advice groups. Genotype-based personalised advice for the prevention of obesity did not affect healthy eating motivation in this group of healthy, young adults.

Through the Lens of Fairness: A Qualitative Investigation into Co‐workers’ Perceptions of I‐deals

AbstractAmidst the rapidly evolving work environment that increasingly necessitates personalized approaches towards employees, there is a pressing need to broaden our understanding of idiosyncratic deals (i‐deals) and their third‐party implications in the workplace. Although immediate responses to i‐deals often hinge on equity and social comparison, this approach does not fully explain co‐workers' perceptions of others' i‐deals. By reconciling various theoretical accounts, we propose that co‐workers' perceptions of i‐deals largely depend on the implementation of i‐deals and related justice perceptions of co‐workers. However, our understanding of how i‐deal implementation shapes co‐workers' perceptions of i‐deals from a justice perspective remains limited. Therefore, we conducted an inductive multiple‐case study through 48 in‐depth interviews involving managers, i‐deal receivers (i‐dealers), and co‐workers. Building on our findings, we develop an emergent framework that explains how the implementation of i‐deals, when viewed through the lens of organizational justice, can shape co‐workers' perceptions of i‐deals. This novel perspective enriches the i‐deal literature by offering a fresh angle on the third‐party implications of i‐deals, emphasizing the significance of their implementation and related fairness perceptions.

Revealing disease subtypes and heterogeneity in common variable immunodeficiency through transcriptomic analysis

Common Variable Immunodeficiency (CVID) is a primary immunodeficiency characterized by reduced levels of specific immunoglobulins, resulting in frequent infections, autoimmune disorders, increased cancer risk, and diminished antibody production despite an adequate B cell count. With its clinical manifestations being highly variable, the classification of CVID, including the widely recognized Freiburg classification, is primarily based on clinical symptoms and genetic variations. Our study aims to refine the classification of CVID by analyzing transcriptomics data to identify distinct disease subtypes. We utilized the GSE51405 dataset, examining transcriptomic profiles from 30 CVID patients without complications. Employing a combination of clustering techniques—KMeans, hierarchical agglomerative clustering, spectral clustering, and Gaussian Mixture models—and differential gene expression analysis with R’s limma package, we integrated molecular findings with demographic data (age and gender) through correlation analysis and identified common genes among clusters. Three distinct clusters of CVID patients were identified using KMeans, Agglomerative Clustering, and Gaussian Mixture Models, highlighting the disease’s heterogeneity. Differential expression analysis unveiled 31 genes with variable expression levels across these clusters. Notably, nine genes (EIF5A, RPL21, ANP32A, DTX3L, NCF2, CDC42EP3, CHP1, FOLR3, and DEFA4) exhibited consistent differential expression across all clusters, independent of demographic factors. The study recommends categorizing patients based on the four genes, NCF2, CHP1, FOLR3, and DEFA4—as they may assist in prognostic prediction. Transcriptomic analysis of common variable immunodeficiency (CVID) patients identified three distinct clusters based on gene expression, independent of age and gender. Nine differentially expressed genes were identified across these clusters, suggesting potential biomarkers for CVID subtype classification. These findings highlight the genetic heterogeneity of CVID and provide novel insights into disease classification and potential personalized treatment approaches.

Relationship between the expressions of DLL3, ASC1, TTF-1 and Ki-67: First steps of precision medicine at SCLC.

This study presents an observational, cross-sectional analysis of 64 patients diagnosed with small cell lung cancer (SCLC) at a reference laboratory for thoracic pathology between 2022 and 2024. The primary objective was to evaluate the expression of Delta-like ligand 3 (DLL3) and other neuroendocrine markers such as Chromogranin, and Synaptophysin, utilizing both traditional immunohistochemistry and digital pathology tools. Patients were primarily older adults, with a median age of over 71, and most biopsies were obtained from lung parenchyma. Immunohistochemistry (IHC) was performed using specific monoclonal antibodies, with DLL3 showing variable expression across the samples. Notably, DLL3 was expressed in 72.3% of the cases, with varied intensities and a semi-quantitative H-score applied for more nuanced analysis. ASCL1 was expressed in 97% of cases, with the majority considered low-expressors. Only 11% had high expression. TTF-1, traditionally not a conventional marker for the diagnosis of SCLC, was positive in half of the cases, suggesting its potential as a biomarker. The study underscores the significant variability in the expression of neuroendocrine markers in SCLC, with implications for both diagnosis and potential therapeutic targeting. DLL3, particularly, shows promise as a therapeutic target due to its high expression rate in the cohort. The use of digital pathology software QuPath enhanced the accuracy and depth of analysis, allowing for detailed morphometric analysis and potentially informing more personalized treatment approaches. The findings emphasize the need for further research into the role of these markers in the management and treatment of SCLC, considering the poor prognosis and high mortality rate observed in the cohort.

Targeting the Tumour Antigen 5T4 using CAR-T Cells for the Treatment of Acute Myeloid Leukaemia.

Chimeric antigen receptor (CAR) T cells represent a novel targeted approach to overcome deficits in the ability of the host immune system to detect and eradicate tumours. 5T4 is a tumour-associated antigen expressed on the cell surface of most solid tumours. However, very little is known about its expression in haematological malignancies. Herein, we assess the expression of 5T4 in different types of leukaemias, specifically Acute Myeloid Leukaemia (AML), and normal haematopoietic stem cells (HSCs). We also provide an in vitro assessment of safety and efficacy of 5T4-targeting CAR-T cells against HSCs and AML tumour cell lines. 5T4 expression was seen in about 50% of AML cases, specifically AML with mutated NPM1, AML-MR and NOS. 5T4 CAR-T cells efficiently and specifically killed AML tumour cell lines, including the Leukaemic Stem Cells. Co-culture of 5T4 CAR-T cells with HSCs from healthy donors showed no impact on subsequent colony formation, thus confirming the safety profile of 5T4. A murine model for AML demonstrated that CAR-T cells recognise and kill in vivo 5T4-expressing tumour cells. These results highlight 5T4 as a promising target for immune intervention in AML and that CAR-T cells can be considered a powerful personalised therapeutic approach to treat AML.

Treatment of myasthenia gravis in france: A retrospective claims database study (STAMINA).

This study aimed to describe treatment patterns in patients with myasthenia gravis (MG) in France. A retrospective cohort analysis was performed using the French National Health Data System (SNDS) database between 2008 and 2019. MG patients were identified using ICD-10 codes during hospitalization and/or long-term disease. We defined two adult subpopulations: a prevalent MG population of patients alive on 31/12/2019and an incident population of newly identified patients with MG in 2012 and 2013. Among the 22,079 prevalent patients, 53.1% (n = 11,498) received at least one chronic MG treatment in 2019. Among these treated patients, 52.5% received Acetylcholinesterase inhibitors (AChEIs) only, 10.2% were treated with corticosteroids (CS) ± AChEIs, 7.3% with non-steroidal immunosuppressive treatments (NSIST) and CS, 24.2% with NSIST w/o CS, and 5.8% received immunoglobulin and/or plasma exchange. Among the 2,661 incident patients, 84.6% received at least one chronic MG treatment over the 6-year follow-up period, and among them, 79.0% had at least one treatment category change. During the first semester of follow-up, 28.1% of patients were treated with an immunomodulator (CS, NSIST). Among patients starting treatment with immunomodulator, the proportion of those treated with CS decreased from 35.3% at initiation to 10.9% at 6years. This study illustrates the complexity of MG management. Significant CS sparing was observed over time. The frequent treatment changes especially in patients with an immunomodulator treatment reflect the high variability of the disease severity. The need for personalised treatment approaches in the management of MG to reduce the burden of disease remains.

LEADERSHIP AND MANAGEMENT IN A MODERN ORGANIZATION: A REVIEW OF CURRENT APPROACHES

The article presents the author's vision of the problem of loss of effectiveness of traditional leadership approaches in managing a modern organization and the need to search for and implement new approaches. It is proposed to shift the focus of attention from classical approaches: from the position of personal qualities (personalistic), behavioral approach and situational approach, justifying the decrease in the results of their use in the activities of managers, to current approaches to organizational leadership, which are offered by modern scientists of the twenty-first century. Based on the analysis of publications in the scientific electronic library Elibrary.ru, such types of leadership as transformational, transactional, charismatic, team, divided, visionary were established. The features of each approach are identified and described. The concept of the terms “leadership”, “leader” (“manager”), “management” is given. The question is raised about the importance of a manager’s understanding of the changing needs and expectations of modern subordinates: the reality of our time is that there is a great need for a flexible work schedule and the ability to work remotely. A vision of what qualities and skills a modern leader should have is proposed, the need to develop emotional intelligence and the importance of changing the way of building communication with modern subordinates is highlighted. Attention is focused on the fact that the forms and methods of training leaders-managers in modern organizations require constant attention and regular improvement. Using the research results in the practical sphere of activity of managers will make it possible to increase the effectiveness of management in the context of the development of the digital economy within the country, improvement of technologies, artificial intelligence, as well as the influence of foreign policy events and increasing sanctions.

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

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

Immune-reactive tumor organoids system to determine the effects of microbial metabolites on cancer immunity and immunotherapies

Immunotherapies are a revolutionary approach to treating cancer by utilizing the body’s immune system to target and combat cancer cells. This approach offers promising alternatives to traditional chemotherapies. Its potential to induce long-lasting remissions and specificity for cancer cells, which minimizes side effects, makes it a cutting-edge treatment with tremendous potential. With the increase of the clinical usage of immunotherapy, evidence emerges of the microbiome’s impact on both tumor growth and response to immunotherapy. The proposed involvement of the microbiome can change treatment efficacy by altering drug metabolism and reshaping the immune system response. Understanding the specific interactions between tumor cells, immune cells, and the microbiome is a critical step in the advancement of immunotherapy. To study the complex interaction between cancer immunity and the microbiome, various preclinical in vivo and in vitro models have been developed. We have recently described the use of an ex vivo preclinical model for anti-cancer treatment outcome prediction –tumor tissue equivalents (organoids). Specifically, immune-reactive tumor organoids are proposed as a novel tool for understanding how the microbiome influences cancer immunity and immunotherapy. More importantly, this platform can utilize patient samples to dissect patient-specific elements regulating cancer immune response and microbiome influence. This review presents the rationale for using the immune-reactive tumor organoids model to study the interactions between the microbiome and cancer immunotherapy. It will discuss available components of the model and analyze their interplay, summarize relevant experimental data, and assess their validity. Additionally, it explores the potential of immune-reactive organoids for personalized treatment approaches. Understanding the microbiome’s role in immunotherapy outcomes will lead to transformative cancer treatment via a simple change of diet or other microbiome manipulations. Ongoing research on microbiome-cancer interactions utilizing the described model systems will lead to innovative treatment strategies and improved patient outcomes.

Tissue-based Profiling Techniques to Achieve Precision Medicine in Cancer: Opportunities and Challenges in Melanoma.

Immunotherapies targeting the programmed cell death 1 (PD-1) and cytotoxic T lymphocyte antigen 4 (CTLA-4) checkpoint receptors have revolutionised the treatment of metastatic melanoma. However, half of treated patients do not respond to or eventually progress on standard therapies and many experience adverse events as a result of drug toxicity. The identification of accurate biomarkers of clinical outcomes are required in order to move away from the one-size-fits-all treatment approach of standard clinical practice, and towards a more personalised approach to enable the administration of the optimal therapy for any given patient and further improve patient outcomes. Recent clinical trials have proven the potential of multi-omics analyses, including genomic, gene expression, and tumour immune profiling, of patients' tumour biopsies, to predict a patient's response to subsequently administered immunotherapies. However, reproducibility of such multi-omics analyses, tissue requirements, and clinical validation have limited the practical application of these approaches in routine clinical workflows. In this review, we discuss several pivotal tissue-based profiling techniques that can be utilised to identify potential genomic, transcriptomic and immune biomarkers predictive of clinical outcomes following treatment with immune checkpoint inhibitors in melanoma. Furthermore, we highlight the key opportunities and challenges associated with the use of each of these techniques. The development and implementation of multimodal predictive models which combine data derived from these various methods is the future for achieving precision medicine for patients with melanoma.

Prediction and validation of pathologic complete response for locally advanced rectal cancer under neoadjuvant chemoradiotherapy based on a novel predictor using interpretable machine learning

BackgroundPrecise evaluation of pathological complete response (pCR) is essential for determining the prognosis of patients with locally advanced rectal cancer (LARC) undergoing neoadjuvant chemoradiotherapy (NCRT) and can offer clues for the selection of subsequent treatment strategies. Most current predictive models for pCR focus primarily on pre-treatment factors, neglecting the dynamic systemic changes that occur during neoadjuvant chemoradiotherapy, and are constrained by low accuracy and lack of integrity. PurposeThis study devised a novel predictor of pCR using dynamic alterations in systemic inflammation-nutritional marker indexes (SINI) during neoadjuvant therapy and developed a machine-learning model to predict pCR. MethodsTwo cohorts of patients with LARC from center one from 2012 to 2017 and from center two from 2020 to 2023 were integrated for analysis. This study compared dynamic changes in blood indexes before and after neoadjuvant therapy and surgical operation. A least absolute shrinkage and selection operator (LASSO) regression analysis was conducted to mitigate collinearity and identify key indexes, constructing the SINI. Univariate and multiple logistic regression analyses were used to identify the independent risk factors associated with pCR. Additionally, 10 machine learning algorithms were employed to develop predictive models to assess risk. The hyperparameters of the machine learning models were optimized using a random search and 10-fold cross-validation. The models were assessed by examining various metrics, including the area under the receiver operating characteristic curves (AUC), the area under the precision-recall curve (AUPRC), decision curve analysis, calibration curves, and the precision and accuracy of the internal and external validation cohorts. Additionally, Shapley's additive explanations (SHAP) were employed to interpret the machine learning models. ResultsThe study cohort comprised 677 patients from the center one and 224 patients from the center two. Six key indexes were identified, and a predictive index, SINI, was constructed. Univariate and multiple logistic regression analyses revealed that SINI, clinical T-stage, clinical N-stage, tumor size, and the distance from the anal verge were independent risk factors for pCR in patients with LARC following NCRT. The mean AUC value of the extreme gradient boosting (XGB) model in the 10-fold cross-validation of the training set was 0.877. The XGB model demonstrated superior performance in the internal and external validation sets. Specifically, in the internal test set, the XGB model achieved an AUC of 0.86, AUPRC of 0.707, accuracy of 0.82, and precision of 0.80. In the external validation set, the XGB model exhibited an AUC of 0.83, AUPRC of 0.702, accuracy of 0.81, and precision of 0.81. Additionally, the predictions generated by the XGB model were analyzed using SHAP. ConclusionThis study involved developing and validating an XGB model using SINI to predict pCR in patients with LARC. Besides, a SINI-based machine learning model shows promise in accurately predicting pCR following NCRT in patients with resectable LARC, offering valuable insights for personalized treatment approaches.

Reintroduction of excluded food triggers as a crucial step in managing patients with food allergies

Introduction. Despite active research into the mechanisms of food allergies (FA), the main approach to managing patients with this condition remains the complete exclusion of causative allergens from the diet for a certain period. At the same time, the question of timely reintroduction of excluded food triggers into the diet is frequently raised, which is important both for maintaining oral tolerance and reducing the negative effects of long-term elimination diets, such as nutritional and eating behavior disorders, as well as financial burdens on families. However, clear recommendations on the reintroduction of previously excluded foods have not existed until recently. Regarding cow’s milk protein allergy (CMPA), such recommendations were provided in 2023 in the consensus document of the World Allergy Organization (WAO) — DRACMA.Aim. The aim of this review is to present current approaches to the reintroduction of food allergens into the diets of patients with food allergies and to evaluate various reintroduction protocols, including those used for cow’s milk protein allergy (CMPA).Material and methods. This review provides a concise summary of current approaches to reintroducing food allergens into the diet, covering both IgE-mediated and non-IgE-mediated forms of food allergy. The advantages of different patient management protocols are discussed, with special attention given to CMPA as one of the most common manifestations of FA in children.Results. An analysis of modern approaches has demonstrated that modern recommendations regarding the reintroduction of allergens, including those presented in the document of the World Allergological Organization — DRACMA for allergy to cow’s milk proteins, allow for a more personalized and safe approach to the reintroduction of allergens, which helps reduce risks and maintain food tolerance.Conclusions. The introduction of new guidelines for the reintroduction of food allergens is an important step in managing patients with food allergies. These recommendations provide a more personalized approach to treating food-allergic patients, including those with cow’s milk protein allergy, reducing the risks associated with reintroducing allergens into the diet. They also help to mitigate the negative effects of elimination diets and maintain oral tolerance in patients, which is particularly important for children with FA.

Investigate the association between genetic polymorphisms of ACE and ACE-2 with some biomarkers in Iraqi patients with COVID-19.

BackgroundThe angiotensin-converting enzyme 2 (ACE2) receptor plays a critical role in mediating SARS-CoV-2 infection. Understanding the genetic factors influencing COVID-19 severity is crucial for developing effective treatment strategies. This study aimed to investigate the association between genetic polymorphism of the ACE gene, specifically the insertion/deletion (I/D) polymorphism in the promoter region, and clinical parameters in COVID-19 patients. MethodsA case-control study was conducted with 225 participants from an Iraqi population. Blood samples were collected for hematological analysis and ACE genotyping. The association between ACE genotypes (DD, ID, II), demographic factors, and clinical parameters, including D-dimer, ferritin, lactate dehydrogenase (LDH), C-reactive protein (CRP), white blood cell (WBC) count, lymphocyte count, packed cell volume (PCV), red blood cell (RBC) count, hemoglobin (HB), and platelet count, was assessed. ResultsCOVID-19 patients exhibited significant differences compared to controls regarding D-dimer, ferritin, LDH, CRP, WBC, lymphocytes, PCV, and RBC (P < 0.05), while no significant differences were found for HB and platelet counts. The DD genotype was predominant (43.11 %), followed by ID (39.56 %) and II (17.33 %). Notably, a significant association was observed between D-dimer levels and ACE genotype (P < 0.0001), with higher levels observed in individuals with the DD genotype. Additionally, a significant correlation was found between ACE genotype and sex (P = 0.0163). No significant association was observed between genotype and ICU admission or lung CT severity. ConclusionOur findings suggest a potential link between the ACE D/D genotype and elevated D-dimer levels in COVID-19 patients, indicating a potential role for ACE gene polymorphism in influencing disease severity. Further research is warranted to elucidate the underlying mechanisms and explore the potential for personalized treatment approaches based on ACE genotype.