Personalized Risk Schemes and Machine Learning to Empower Genomic Prognostication Models in Myelodysplastic Syndromes

Single-cell RNA Sequencing Contributes to the Treatment of Acute Myeloid Leukaemia With Hematopoietic Stem Cell Transplantation, Chemotherapy, and Immunotherapy

Acute myeloid leukemia (AML) is caused by altered maturation and differentiation of myeloid blasts, as well as transcriptional/epigenetic alterations and impaired apoptosis, all of which lead to excessive proliferation of malignant blood cells in the bone marrow. It is these mutations that cause tumor heterogeneity, which is linked to a higher risk of relapse and death and makes anti-AML treatments like HSCT, chemotherapy, and immunotherapy (ICI, CAR T-cell-based therapies, and cancer vaccines) less effective. Single-cell RNA sequencing (scRNA-seq) also makes it possible to find cellular subclones and profile tumors, which opens up new diagnostic and therapeutic targets for better AML management. The HSCT process works better when genetic and transcriptional information about the patient and donor stem cells is collected. This saves time and lowers the risk of harmful side effects happening in the body.

Single cell RNA sequencing improves the next generation of approaches to AML treatment: challenges and perspectives

Acute myeloid leukemia (AML) is caused by altered maturation and differentiation of myeloid blasts, as well as transcriptional/epigenetic alterations, all leading to excessive proliferation of malignant blood cells in the bone marrow. Tumor heterogeneity due to the acquisition of new somatic alterations leads to a high rate of resistance to current therapies or reduces the efficacy of hematopoietic stem cell transplantation (HSCT), thus increasing the risk of relapse and mortality. Single-cell RNA sequencing (scRNA-seq) will enable the classification of AML and guide treatment approaches by profiling patients with different facets of the same disease, stratifying risk, and identifying new potential therapeutic targets at the time of diagnosis or after treatment. ScRNA-seq allows the identification of quiescent stem-like cells, and leukemia stem cells responsible for resistance to therapeutic approaches and relapse after treatment. This method also introduces the factors and mechanisms that enhance the efficacy of the HSCT process. Generated data of the transcriptional profile of the AML could even allow the development of cancer vaccines and CAR T-cell therapies while saving valuable time and alleviating dangerous side effects of chemotherapy and HSCT in vivo. However, scRNA-seq applications face various challenges such as a large amount of data for high-dimensional analysis, technical noise, batch effects, and finding small biological patterns, which could be improved in combination with artificial intelligence models.

Machine learning prediction of the failure of high-flow nasal oxygen therapy in patients with acute respiratory failure

Acute respiratory failure (ARF) is a prevalent and serious condition in intensive care unit (ICU), often associated with high mortality rates. High-flow nasal oxygen (HFNO) therapy has gained popularity for treating ARF in recent years. However, there is a limited understanding of the factors that predict HFNO failure in ARF patients. This study aimed to explore early indicators of HFNO failure in ARF patients, utilizing machine learning (ML) algorithms to more accurately pinpoint individuals at elevated risk of HFNO failure. Utilizing ML algorithms, we developed seven predictive models. Their performance was evaluated using various metrics, including the area under the receiver operating characteristic curve, calibration curve, and precision recall curve. The study enrolled 700 patients, with 490 in the training group and 210 in the validation group. The overall HFNO failure rate was 14.1% among the 700 patients. The ML algorithms demonstrated robust performance in our study. This research underscores the potential of ML techniques in creating clinically relevant models for predicting HFNO outcomes in ARF patients. These models could play a pivotal role in enhancing the risk management of HFNO, leading to more patient-centered and personalized care approaches.

When to use which molecular prognostic scoring system in the management of patients with MDS?

Myelodysplastic syndromes/neoplasms (MDS) are a heterogeneous group of hematopoietic cancers characterized by recurrent molecular alterations driving the disease pathogenesis with a variable propensity for progression to acute myeloid leukemia (AML). Clinical decision making for MDS relies on appropriate risk stratification at diagnosis, with higher-risk patients requiring more intensive therapy. The conventional clinical prognostic systems including the International Prognostic Scoring System (IPSS) and its revised version (IPSS-R) have dominated the risk stratification of MDS from 1997 until 2022. Concurrently, the use of next-generation sequencing has revolutionized the field by revealing multiple recurrent genetic mutations, which correlate with phenotype and prognosis. Significant efforts have been made to formally incorporate molecular data into prognostic tools to improve proper risk identification and personalize treatment strategies. In this review, we will critically compare the available molecular scoring systems for MDS focusing on areas of progress and potential limitations that can be improved in subsequent revisions of these tools.

The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review

Background

Artificial intelligence (AI) is a broad outlet of computer science aimed at constructing machines capable of simulating and performing tasks usually done by human beings. The aim of this scoping review is to map existing evidence on the use of AI in the delivery of medical care.

Methods

We searched PubMed and Scopus in March 2022, screened identified records for eligibility, assessed full texts of potentially eligible publications, and extracted data from included studies in duplicate, resolving differences through discussion, arbitration, and consensus. We then conducted a narrative synthesis of extracted data.

Results

Several AI methods have been used to detect, diagnose, classify, manage, treat, and monitor the prognosis of various health issues. These AI models have been used in various health conditions, including communicable diseases, non-communicable diseases, and mental health.

Conclusions

Presently available evidence shows that AI models, predominantly deep learning, and machine learning, can significantly advance medical care delivery regarding the detection, diagnosis, management, and monitoring the prognosis of different illnesses.

Artificial Intelligence-Assisted Diagnostic Cytology and Genomic Testing for Hematologic Disorders

Artificial intelligence (AI) is a rapidly evolving field of computer science that involves the development of computational programs that can mimic human intelligence. In particular, machine learning and deep learning models have enabled the identification and grouping of patterns within data, leading to the development of AI systems that have been applied in various areas of hematology, including digital pathology, alpha thalassemia patient screening, cytogenetics, immunophenotyping, and sequencing. These AI-assisted methods have shown promise in improving diagnostic accuracy and efficiency, identifying novel biomarkers, and predicting treatment outcomes. However, limitations such as limited databases, lack of validation and standardization, systematic errors, and bias prevent AI from completely replacing manual diagnosis in hematology. In addition, the processing of large amounts of patient data and personal information by AI poses potential data privacy issues, necessitating the development of regulations to evaluate AI systems and address ethical concerns in clinical AI systems. Nonetheless, with continued research and development, AI has the potential to revolutionize the field of hematology and improve patient outcomes. To fully realize this potential, however, the challenges facing AI in hematology must be addressed and overcome.

Molecular Aspects of Hematological Malignancies and Benign Hematological Disorders

Hematology represents a dynamic specialty in clinical medicine that requires solid knowledge of normal and pathological hematopoiesis, cytomorphology, pathology, immunology, genetics and molecular biology [...].

How I Manage Transplant Ineligible Patients with Myelodysplastic Neoplasms

Myelodysplastic neoplasms, formerly known as myelodysplastic syndromes (MDS), represent a group of clonal disorders characterized by a high degree of clinical and molecular heterogeneity, and an invariable tendency to progress to acute myeloid leukemia. MDS typically present in the elderly with cytopenias of different degrees and bone marrow dysplasia, the hallmarks of the disease. Allogeneic hematopoietic stem cell transplant is the sole curative approach to date. Nonetheless, given the disease's demographics, only a minority of patients can benefit from this procedure. Currently used prognostic schemes such as the Revised International Prognostic Scoring System (R-IPSS), and most recently the molecular IPSS (IPSS-M), guide clinical management by dividing MDS into two big categories: lower- and higher-risk cases, based on a cut-off score of 3.5. The main clinical problem of the lower-risk group is represented by the management of cytopenias, whereas the prevention of secondary leukemia progression is the goal for the latter. Herein, we discuss the non-transplant treatment of MDS, focusing on current practice and available therapeutic options, while also presenting new investigational agents potentially entering the MDS therapeutic arsenal in the near future.

The Use of Machine Learning Techniques to Predict Deep Vein Thrombosis in Rehabilitation Inpatients

BACKGROUND

Rehabilitation is crucial to recovering patients' dysfunction, improving their life quality, and promoting an early return to their family and society. In China, most patients in rehabilitation units are patients transferred from neurology, neurosurgery, and orthopedics, and most of these patients face problems such as continuously bedridden or varying degrees of limb dysfunction, all of which are risk factors for deep venous thrombosis. The formation of deep venous thrombosis can delay the recovery process and result in significant morbidity, mortality, and higher healthcare costs, so early detection and individualized treatment are needed. Machine learning algorithms can help develop more precise prognostic models, which can be of great significance in the development of rehabilitation training programs. In this study, we aimed to develop a model of deep venous thrombosis for inpatients in the Department of Rehabilitation Medicine at the Affiliated Hospital of Nantong University using machine learning methods.

METHODS

We analyzed and compared 801 patients in the Department of Rehabilitation Medicine using machine learning. Support vector machine, logistic regression, decision tree, random forest classifier, and artificial neural network were used to build models.

RESULTS

Artificial neural network was the better predictor than other traditional machine learnings. D-dimer levels, bedridden time, Barthel Index, and fibrinogen degradation products were common predictors of adverse outcomes in these models.

CONCLUSIONS

Risk stratification can help healthcare practitioners to achieve improvements in clinical efficiency and specify appropriate rehabilitation training programs.