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1.
Curr Oncol ; 28(3): 2248-2259, 2021 06 17.
Artigo em Inglês | MEDLINE | ID: mdl-34204531

RESUMO

Patients awaiting cancer treatment were classified as "vulnerable" and advised to shield to protect themselves from exposure to coronavirus during the pandemic. These measures can negatively impact patients. We sought to establish the feasibility and effects of a telehealth-delivered home-based prehabilitation program during the pandemic. Eligible patients were referred from multiple centers to a regional prehabilitation unit providing home-based prehabilitation. The enrolled patients received telehealth-delivered prehabilitation prior to surgery and/or during non-surgical cancer treatment, which included personalized training exercises, dietary advice, medical optimization therapies, and psychological support. The primary outcome was to investigate the feasibility of our program. The secondary outcome was to investigate the relationship between our program and patient-reported outcomes (PROs). The patients completed two questionnaires (the EQ-5D-3L and the FACIT-Fatigue Scale) pre- and post-intervention. A total of 182 patients were referred during the study period. Among the 139 (76%) patients that were enrolled, 100 patients completed the program, 24 patients have still to complete, and 15 have discontinued. A total of 66 patients were able to return completed questionnaires. These patients were recruited from colorectal, urology, breast, and cardiothoracic centers. The patients significantly improved their self-perceived health (p = 0.001), and fatigue (p = 0.000). Home-based prehabilitation is a feasible intervention. The PROs improved post-intervention.


Assuntos
COVID-19/epidemiologia , Neoplasias/terapia , Telemedicina/métodos , Idoso , Inglaterra , Exercício Físico , Estudos de Viabilidade , Feminino , Grupos Focais , Humanos , Masculino , Pessoa de Meia-Idade , Neoplasias/psicologia , Medicina de Precisão/métodos , Cuidados Pré-Operatórios , Exercício Pré-Operatório , Estudos Prospectivos , Qualidade de Vida , Resultado do Tratamento
2.
Int J Mol Sci ; 22(12)2021 Jun 17.
Artigo em Inglês | MEDLINE | ID: mdl-34204452

RESUMO

Intercellular communication governs multicellular interactions in complex organisms. A variety of mechanisms exist through which cells can communicate, e.g., cell-cell contact, the release of paracrine/autocrine soluble molecules, or the transfer of extracellular vesicles (EVs). EVs are membrane-surrounded structures released by almost all cell types, acting both nearby and distant from their tissue/organ of origin. In the kidney, EVs are potent intercellular messengers released by all urinary system cells and are involved in cell crosstalk, contributing to physiology and pathogenesis. Moreover, urine is a reservoir of EVs coming from the circulation after crossing the glomerular filtration barrier-or originating in the kidney. Thus, urine represents an alternative source for biomarkers in kidney-related diseases, potentially replacing standard diagnostic techniques, including kidney biopsy. This review will present an overview of EV biogenesis and classification and the leading procedures for isolating EVs from body fluids. Furthermore, their role in intra-nephron communication and their use as a diagnostic tool for precision medicine in kidney-related disorders will be discussed.


Assuntos
Biomarcadores/urina , Vesículas Extracelulares/metabolismo , Nefropatias/metabolismo , Animais , Comunicação Celular , Micropartículas Derivadas de Células/metabolismo , Fracionamento Químico , Gerenciamento Clínico , Suscetibilidade a Doenças , Exossomos/metabolismo , Humanos , Nefropatias/diagnóstico , Nefropatias/etiologia , Nefropatias/urina , Biópsia Líquida/métodos , Medicina de Precisão/métodos , Urinálise/métodos
3.
Int J Mol Sci ; 22(12)2021 Jun 18.
Artigo em Inglês | MEDLINE | ID: mdl-34207103

RESUMO

Ovarian cancer response to immunotherapy is limited; however, the evaluation of sensitive/resistant target treatment subpopulations based on stratification by tumor biomarkers may improve the predictiveness of response to immunotherapy. These markers include tumor mutation burden, PD-L1, tumor-infiltrating lymphocytes, homologous recombination deficiency, and neoantigen intratumoral heterogeneity. Future directions in the treatment of ovarian cancer include the utilization of these biomarkers to select ideal candidates. This paper reviews the role of immunotherapy in ovarian cancer as well as novel therapeutics and study designs involving tumor biomarkers that increase the likelihood of success with immunotherapy in ovarian cancer.


Assuntos
Imunoterapia , Neoplasias Ovarianas/terapia , Medicina de Precisão , Antígenos de Neoplasias/imunologia , Biomarcadores Tumorais , Ensaios Clínicos como Assunto , Gerenciamento Clínico , Suscetibilidade a Doenças , Feminino , Humanos , Imunoterapia/efeitos adversos , Imunoterapia/métodos , Terapia de Alvo Molecular , Neoplasias Ovarianas/diagnóstico , Neoplasias Ovarianas/etiologia , Neoplasias Ovarianas/mortalidade , Medicina de Precisão/efeitos adversos , Medicina de Precisão/métodos , Resultado do Tratamento
4.
Int J Mol Sci ; 22(12)2021 Jun 17.
Artigo em Inglês | MEDLINE | ID: mdl-34204274

RESUMO

Nonalcoholic fatty liver disease (NAFLD) is the leading cause of liver disease worldwide, affecting both adults and children and will result, in the near future, as the leading cause of end-stage liver disease. Indeed, its prevalence is rapidly increasing, and NAFLD is becoming a major public health concern. For this reason, great efforts are needed to identify its pathogenetic factors and new therapeutic approaches. In the past decade, enormous advances understanding the gut-liver axis-the complex network of cross-talking between the gut, microbiome and liver through the portal circulation-have elucidated its role as one of the main actors in the pathogenesis of NAFLD. Indeed, evidence shows that gut microbiota is involved in the development and progression of liver steatosis, inflammation and fibrosis seen in the context of NAFLD, as well as in the process of hepatocarcinogenesis. As a result, gut microbiota is currently emerging as a non-invasive biomarker for the diagnosis of disease and for the assessment of its severity. Additionally, to its enormous diagnostic potential, gut microbiota is currently studied as a therapeutic target in NAFLD: several different approaches targeting the gut homeostasis such as antibiotics, prebiotics, probiotics, symbiotics, adsorbents, bariatric surgery and fecal microbiota transplantation are emerging as promising therapeutic options.


Assuntos
Suscetibilidade a Doenças , Trato Gastrointestinal/metabolismo , Fígado/metabolismo , Hepatopatia Gordurosa não Alcoólica/etiologia , Hepatopatia Gordurosa não Alcoólica/metabolismo , Transdução de Sinais , Ácidos e Sais Biliares/metabolismo , Biomarcadores , Gerenciamento Clínico , Metabolismo Energético , Microbioma Gastrointestinal , Trato Gastrointestinal/microbiologia , Humanos , Fígado/patologia , Hepatopatia Gordurosa não Alcoólica/patologia , Hepatopatia Gordurosa não Alcoólica/terapia , Permeabilidade , Medicina de Precisão/métodos
5.
Nat Commun ; 12(1): 4228, 2021 07 09.
Artigo em Inglês | MEDLINE | ID: mdl-34244484

RESUMO

Homozygous deletion of methylthioadenosine phosphorylase (MTAP) in cancers such as glioblastoma represents a potentially targetable vulnerability. Homozygous MTAP-deleted cell lines in culture show elevation of MTAP's substrate metabolite, methylthioadenosine (MTA). High levels of MTA inhibit protein arginine methyltransferase 5 (PRMT5), which sensitizes MTAP-deleted cells to PRMT5 and methionine adenosyltransferase 2A (MAT2A) inhibition. While this concept has been extensively corroborated in vitro, the clinical relevance relies on exhibiting significant MTA accumulation in human glioblastoma. In this work, using comprehensive metabolomic profiling, we show that MTA secreted by MTAP-deleted cells in vitro results in high levels of extracellular MTA. We further demonstrate that homozygous MTAP-deleted primary glioblastoma tumors do not significantly accumulate MTA in vivo due to metabolism of MTA by MTAP-expressing stroma. These findings highlight metabolic discrepancies between in vitro models and primary human tumors that must be considered when developing strategies for precision therapies targeting glioblastoma with homozygous MTAP deletion.


Assuntos
Neoplasias Encefálicas/genética , Encéfalo/patologia , Desoxiadenosinas/metabolismo , Glioblastoma/genética , Purina-Núcleosídeo Fosforilase/deficiência , Tionucleosídeos/metabolismo , Antineoplásicos/farmacologia , Antineoplásicos/uso terapêutico , Encéfalo/efeitos dos fármacos , Neoplasias Encefálicas/tratamento farmacológico , Neoplasias Encefálicas/patologia , Linhagem Celular Tumoral , Meios de Cultivo Condicionados/metabolismo , Desoxiadenosinas/análise , Feminino , Secções Congeladas , Glioblastoma/tratamento farmacológico , Glioblastoma/patologia , Homozigoto , Humanos , Metabolômica , Metionina Adenosiltransferase/metabolismo , Terapia de Alvo Molecular/métodos , Medicina de Precisão/métodos , Proteína-Arginina N-Metiltransferases/metabolismo , Purina-Núcleosídeo Fosforilase/genética , Deleção de Sequência , Tionucleosídeos/análise , Ensaios Antitumorais Modelo de Xenoenxerto
6.
J Clin Pathol ; 74(7): 429-434, 2021 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-34117103

RESUMO

Clinical workflows in oncology depend on predictive and prognostic biomarkers. However, the growing number of complex biomarkers contributes to costly and delayed decision-making in routine oncology care and treatment. As cancer is expected to rank as the leading cause of death and the single most important barrier to increasing life expectancy in the 21st century, there is a major emphasis on precision medicine, particularly individualisation of treatment through better prediction of patient outcome. Over the past few years, both surgical and pathology specialties have suffered cutbacks and a low uptake of pathology specialists means a solution is required to enable high-throughput screening and personalised treatment in this area to alleviate bottlenecks. Digital imaging in pathology has undergone an exponential period of growth. Deep-learning (DL) platforms for hematoxylin and eosin (H&E) image analysis, with preliminary artificial intelligence (AI)-based grading capabilities of specimens, can evaluate image characteristics which may not be visually apparent to a pathologist and offer new possibilities for better modelling of disease appearance and possibly improve the prediction of disease stage and patient outcome. Although digital pathology and AI are still emerging areas, they are the critical components for advancing personalised medicine. Integration of transcriptomic analysis, clinical information and AI-based image analysis is yet an uncultivated field by which healthcare professionals can make improved treatment decisions in cancer. This short review describes the potential application of integrative AI in offering better detection, quantification, classification, prognosis and prediction of breast and prostate cancer and also highlights the utilisation of machine learning systems in biomarker evaluation.


Assuntos
Inteligência Artificial , Biomarcadores Tumorais/análise , Neoplasias da Mama/diagnóstico , Interpretação de Imagem Assistida por Computador/métodos , Neoplasias da Próstata/diagnóstico , Inteligência Artificial/tendências , Feminino , Humanos , Masculino , Oncologia/métodos , Oncologia/tendências , Patologia Clínica/métodos , Patologia Clínica/tendências , Medicina de Precisão/métodos , Medicina de Precisão/tendências
7.
Nat Commun ; 12(1): 2493, 2021 05 03.
Artigo em Inglês | MEDLINE | ID: mdl-33941778

RESUMO

The need for precision medicine approaches to monitor health and disease makes it important to develop sensitive and accurate assays for proteome profiles in blood. Here, we describe an approach for plasma profiling based on proximity extension assay combined with next generation sequencing. First, we analyze the variability of plasma profiles between and within healthy individuals in a longitudinal wellness study, including the influence of genetic variations on plasma levels. Second, we follow patients newly diagnosed with type 2 diabetes before and during therapeutic intervention using plasma proteome profiling. The studies show that healthy individuals have a unique and stable proteome profile and indicate that a panel of proteins could potentially be used for early diagnosis of diabetes, including stratification of patients with regards to response to metformin treatment. Although validation in larger cohorts is needed, the analysis demonstrates the usefulness of comprehensive plasma profiling for precision medicine efforts.


Assuntos
Proteínas Sanguíneas/análise , Diabetes Mellitus Tipo 2/sangue , Diabetes Mellitus Tipo 2/diagnóstico , Plasma/química , Proteoma/análise , Idoso , Diabetes Mellitus Tipo 2/genética , Diagnóstico Precoce , Feminino , Variação Genética/genética , Sequenciamento de Nucleotídeos em Larga Escala , Humanos , Hipoglicemiantes/uso terapêutico , Estudos Longitudinais , Masculino , Metformina/uso terapêutico , Pessoa de Meia-Idade , Medicina de Precisão/métodos , Proteômica/métodos
8.
Life Sci ; 278: 119580, 2021 Aug 01.
Artigo em Inglês | MEDLINE | ID: covidwho-1225325

RESUMO

COVID-19 pandemic is still a major risk to human civilization. Besides the global immunization policy, more than five lac new cases are documented everyday. Some countries newly implement partial/complete nationwid lockdown to mitigate recurrent community spreading. To avoid the new modified stain of SARS-CoV-2 spreading, some countries imposed any restriction on the movement of the citizens within or outside the country. Effective economical point of care diagnostic and therapeutic strategy is vigorously required to mitigate viral spread. Besides struggling with repurposed medicines, new engineered materials with multiple unique efficacies and specific antiviral potency against SARS-CoV-2 infection may be fruitful to save more lives. Nanotechnology-based engineering strategy sophisticated medicine with specific, effective and nonhazardous delivery mechanism for available repurposed antivirals as well as remedial for associated diseases due to malfeasance in immuno-system e.g. hypercytokinaemia, acute respiratory distress syndrome. This review will talk about gloomy but critical areas for nanoscientists to intervene and will showcase about the different laboratory diagnostic, prognostic strategies and their mode of actions. In addition, we speak about SARS-CoV-2 pathophysiology, pathogenicity and host specific interation with special emphasis on altered immuno-system and also perceptualized, copious ways to design prophylactic nanomedicines and next-generation vaccines based on recent findings.


Assuntos
COVID-19/terapia , Nanomedicina Teranóstica/métodos , Animais , Antivirais/administração & dosagem , Antivirais/uso terapêutico , COVID-19/diagnóstico , COVID-19/imunologia , COVID-19/patologia , Vacinas contra COVID-19/administração & dosagem , Vacinas contra COVID-19/uso terapêutico , Sistemas de Liberação de Medicamentos/métodos , Humanos , Imunização/métodos , Nanotecnologia/métodos , Medicina de Precisão/métodos , SARS-CoV-2/efeitos dos fármacos , SARS-CoV-2/imunologia , SARS-CoV-2/isolamento & purificação
9.
Nature ; 594(7862): 265-270, 2021 06.
Artigo em Inglês | MEDLINE | ID: covidwho-1246377

RESUMO

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning-a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine.


Assuntos
Blockchain , Tomada de Decisão Clínica/métodos , Confidencialidade , Conjuntos de Dados como Assunto , Aprendizado de Máquina , Medicina de Precisão/métodos , COVID-19/diagnóstico , COVID-19/epidemiologia , Surtos de Doenças , Feminino , Humanos , Leucemia/diagnóstico , Leucemia/patologia , Leucócitos/patologia , Pneumopatias/diagnóstico , Aprendizado de Máquina/tendências , Masculino , Software , Tuberculose/diagnóstico
10.
J Consult Clin Psychol ; 89(4): 288-300, 2021 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-34014691

RESUMO

OBJECTIVE: Numerous behavioral treatments for alcohol use disorder (AUD) are effective, but there are substantial individual differences in treatment response. This study examines the potential use of new methods for personalized medicine to test for individual differences in the effects of cognitive behavioral therapy (CBT) versus motivational enhancement therapy (MET) and to provide predictions of which will work best for individuals with AUD. We highlight both the potential contribution and the limitations of these methods. METHOD: We performed secondary analyses of abstinence among 1,144 participants with AUD participating in either outpatient or aftercare treatment who were randomized to receive either CBT or MET in Project MATCH. We first obtained predicted individual treatment effects (PITEs), as a function of 19 baseline client characteristics identified a priori by MATCH investigators. Then, we tested for the significance of individual differences and examined the predicted individual differences in abstinence 1 year following treatment. Predictive intervals were estimated for each individual to determine if they were 80% more likely to achieve abstinence in one treatment versus the other. RESULTS: Results indicated that individual differences in the likelihood of abstinence at 1 year following treatment were significant for those in the outpatient sample, but not for those in the aftercare sample. Individual predictive intervals showed that 37% had a better chance of abstinence with CBT than MET, and 16% had a better chance of abstinence with MET. Obtaining predictions for a new individual is demonstrated. CONCLUSIONS: Personalized medicine methods, and PITE in particular, have the potential to identify individuals most likely to benefit from one versus another intervention. New personalized medicine methods play an important role in putting together differential effects due to previously identified variables into one prediction designed to be useful to clinicians and clients choosing between treatment options. (PsycInfo Database Record (c) 2021 APA, all rights reserved).


Assuntos
Alcoolismo/terapia , Terapia Comportamental/métodos , Individualidade , Medicina de Precisão/métodos , Adulto , Assistência ao Convalescente , Idoso , Abstinência de Álcool/estatística & dados numéricos , Assistência Ambulatorial , Terapia Comportamental/estatística & dados numéricos , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Medicina de Precisão/estatística & dados numéricos , Probabilidade , Adulto Jovem
11.
Int J Mol Sci ; 22(9)2021 Apr 29.
Artigo em Inglês | MEDLINE | ID: mdl-33946818

RESUMO

Since 2010, several treatment options have been available for men with metastatic castration-resistant prostate cancer (mCRPC), including immunotherapeutic agents, although the clinical benefit of these agents remains inconclusive in unselected mCRPC patients. In recent years, however, immunotherapy has re-emerged as a promising therapeutic option to stimulate antitumor immunity, particularly with the use of immune checkpoint inhibitors (ICIs), such as PD-1/PD-L1 and CTLA-4 inhibitors. There is increasing evidence that ICIs may be especially beneficial in specific subgroups of patients with high PD-L1 tumor expression, high tumor mutational burden, or tumors with high microsatellite instability/mismatch repair deficiency. If we are to improve the efficacy of ICIs, it is crucial to have a better understanding of the mechanisms of resistance to ICIs and to identify predictive biomarkers to determine which patients are most likely to benefit. This review focuses on the current status of ICIs for the treatment of mCRPC (either as monotherapy or in combination with other drugs), mechanisms of resistance, potential predictive biomarkers, and future challenges in the management of mCRPC.


Assuntos
Adenocarcinoma/secundário , Antineoplásicos Imunológicos/uso terapêutico , Antígeno B7-H1/antagonistas & inibidores , Inibidores de Checkpoint Imunológico/uso terapêutico , Imunoterapia/métodos , Neoplasias de Próstata Resistentes à Castração/terapia , Adenocarcinoma/terapia , Antineoplásicos Imunológicos/administração & dosagem , Protocolos de Quimioterapia Combinada Antineoplásica/uso terapêutico , Biomarcadores , Ensaios Clínicos como Assunto , Reparo do DNA/genética , Resistencia a Medicamentos Antineoplásicos , Previsões , Humanos , Inibidores de Checkpoint Imunológico/administração & dosagem , Masculino , Proteínas de Membrana Transportadoras/efeitos dos fármacos , Estudos Multicêntricos como Assunto , Proteínas de Neoplasias/antagonistas & inibidores , Compostos Organoplatínicos/administração & dosagem , Inibidores de Poli(ADP-Ribose) Polimerases/uso terapêutico , Medicina de Precisão/métodos , Receptor de Morte Celular Programada 1/antagonistas & inibidores , Taxoides/administração & dosagem
12.
Medicine (Baltimore) ; 100(20): e25994, 2021 May 21.
Artigo em Inglês | MEDLINE | ID: mdl-34011092

RESUMO

ABSTRACT: In precision oncology, immune check point blockade therapy has quickly emerged as novel strategy by its efficacy, where programmed death ligand 1 (PD-L1) expression is used as a clinically validated predictive biomarker of response for the therapy. Automating pathological image analysis and accelerating pathology evaluation is becoming an unmet need. Artificial Intelligence and deep learning tools in digital pathology have been studied in order to evaluate PD-L1 expression in PD-L1 immunohistochemistry image. We proposed a Dual-scale Categorization (DSC)-based deep learning method that employed 2 VGG16 neural networks, 1 network for 1 scale, to critically evaluate PD-L1 expression. The DSC-based deep learning method was tested in a cohort of 110 patients diagnosed as non-small cell lung cancer. This method showed a concordance of 88% with pathologist, which was higher than concordance of 83% of 1-scale categorization-based method. Our results show that the DSCbased method can empower the deep learning application in digital pathology and facilitate computer-aided diagnosis.


Assuntos
Antígeno B7-H1/análise , Biomarcadores Tumorais/análise , Carcinoma Pulmonar de Células não Pequenas/diagnóstico , Diagnóstico por Computador/métodos , Neoplasias Pulmonares/diagnóstico , Antígeno B7-H1/antagonistas & inibidores , Antígeno B7-H1/genética , Biomarcadores Tumorais/antagonistas & inibidores , Biomarcadores Tumorais/genética , Carcinoma Pulmonar de Células não Pequenas/tratamento farmacológico , Carcinoma Pulmonar de Células não Pequenas/genética , Carcinoma Pulmonar de Células não Pequenas/patologia , Aprendizado Profundo , Regulação Neoplásica da Expressão Gênica , Humanos , Inibidores de Checkpoint Imunológico/farmacologia , Inibidores de Checkpoint Imunológico/uso terapêutico , Imuno-Histoquímica , Pulmão/imunologia , Pulmão/patologia , Neoplasias Pulmonares/tratamento farmacológico , Neoplasias Pulmonares/genética , Neoplasias Pulmonares/patologia , Seleção de Pacientes , Medicina de Precisão/métodos
14.
Nat Med ; 27(5): 775-784, 2021 05.
Artigo em Inglês | MEDLINE | ID: mdl-33990804

RESUMO

Machine learning techniques have great potential to improve medical diagnostics, offering ways to improve accuracy, reproducibility and speed, and to ease workloads for clinicians. In the field of histopathology, deep learning algorithms have been developed that perform similarly to trained pathologists for tasks such as tumor detection and grading. However, despite these promising results, very few algorithms have reached clinical implementation, challenging the balance between hope and hype for these new techniques. This Review provides an overview of the current state of the field, as well as describing the challenges that still need to be addressed before artificial intelligence in histopathology can achieve clinical value.


Assuntos
Aprendizado Profundo , Patologia Molecular/métodos , Medicina de Precisão/métodos , Algoritmos , Humanos
15.
Artigo em Inglês | MEDLINE | ID: mdl-33975679

RESUMO

Gastric cancer is a major cause of cancer-related morbidity and mortality worldwide. Advances in targeted medical treatment were scarce in the past and challenged by the marked spatial and temporal biological heterogeneity of gastric cancer. Recent molecular profiling studies have increased our understanding of genetic and epigenetic drivers, leading to better patient selection for drug development. Beyond that, immune-related biomarkers were identified, paving the way for future effective immunotherapy. We systematically reviewed articles from PubMed of the past 10 years, and abstracts from annual meetings of ESMO, ASCO and AACR to summarise the current knowledge about targeted and immunotherapy and outline pathways to future personalised therapy of gastric cancer.


Assuntos
Imunoterapia/métodos , Medicina de Precisão/métodos , Neoplasias Gástricas/tratamento farmacológico , Humanos , Neoplasias Gástricas/imunologia
16.
Nature ; 594(7862): 265-270, 2021 06.
Artigo em Inglês | MEDLINE | ID: mdl-34040261

RESUMO

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning-a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine.


Assuntos
Blockchain , Tomada de Decisão Clínica/métodos , Confidencialidade , Conjuntos de Dados como Assunto , Aprendizado de Máquina , Medicina de Precisão/métodos , COVID-19/diagnóstico , COVID-19/epidemiologia , Surtos de Doenças , Feminino , Humanos , Leucemia/diagnóstico , Leucemia/patologia , Leucócitos/patologia , Pneumopatias/diagnóstico , Aprendizado de Máquina/tendências , Masculino , Software , Tuberculose/diagnóstico
17.
Life Sci ; 278: 119580, 2021 Aug 01.
Artigo em Inglês | MEDLINE | ID: mdl-33991549

RESUMO

COVID-19 pandemic is still a major risk to human civilization. Besides the global immunization policy, more than five lac new cases are documented everyday. Some countries newly implement partial/complete nationwid lockdown to mitigate recurrent community spreading. To avoid the new modified stain of SARS-CoV-2 spreading, some countries imposed any restriction on the movement of the citizens within or outside the country. Effective economical point of care diagnostic and therapeutic strategy is vigorously required to mitigate viral spread. Besides struggling with repurposed medicines, new engineered materials with multiple unique efficacies and specific antiviral potency against SARS-CoV-2 infection may be fruitful to save more lives. Nanotechnology-based engineering strategy sophisticated medicine with specific, effective and nonhazardous delivery mechanism for available repurposed antivirals as well as remedial for associated diseases due to malfeasance in immuno-system e.g. hypercytokinaemia, acute respiratory distress syndrome. This review will talk about gloomy but critical areas for nanoscientists to intervene and will showcase about the different laboratory diagnostic, prognostic strategies and their mode of actions. In addition, we speak about SARS-CoV-2 pathophysiology, pathogenicity and host specific interation with special emphasis on altered immuno-system and also perceptualized, copious ways to design prophylactic nanomedicines and next-generation vaccines based on recent findings.


Assuntos
COVID-19/terapia , Nanomedicina Teranóstica/métodos , Animais , Antivirais/administração & dosagem , Antivirais/uso terapêutico , COVID-19/diagnóstico , COVID-19/imunologia , COVID-19/patologia , Vacinas contra COVID-19/administração & dosagem , Vacinas contra COVID-19/uso terapêutico , Sistemas de Liberação de Medicamentos/métodos , Humanos , Imunização/métodos , Nanotecnologia/métodos , Medicina de Precisão/métodos , SARS-CoV-2/efeitos dos fármacos , SARS-CoV-2/imunologia , SARS-CoV-2/isolamento & purificação
18.
Med Sci Monit ; 27: e933088, 2021 May 17.
Artigo em Inglês | MEDLINE | ID: mdl-33994538

RESUMO

Synthetic mRNA and the expression of therapeutic proteins have accelerated vaccine development to prevent infection and heralds a new era in targeted immunotherapy in oncology. Therapeutic mRNA vaccines rely on available tumor tissue for gene sequencing analysis to compare the patient's normal cellular DNA sequences and those of the tumor. Carrier-based mRNA vaccines for cancer immunotherapy are now in development that use delivery systems based on peptides, lipids, polymers, and cationic nano-emulsions. There have also been recent developments in dendritic cell-based mRNA vaccines. For patients with available tumor tissue samples, it is possible to develop mRNA vaccines that result in the expression of tumor antigens by antigen-presenting cells (APCs), resulting in innate and adaptive immune responses. Ongoing developments in mRNA immunotherapy include modifications in the route of administration and combined delivery of multiple mRNA vaccines with checkpoint inhibitors. This Editorial aims to present a brief overview of how mRNA immunotherapy may change the therapeutic landscape of personalized medicine for patients with solid malignant tumors.


Assuntos
Vacinas Anticâncer/imunologia , Neoplasias/imunologia , Neoplasias/terapia , RNA Mensageiro/imunologia , Vacinas Sintéticas/imunologia , Humanos , Imunoterapia/métodos , Oncologia/métodos , Medicina de Precisão/métodos
19.
Oncology ; 99(7): 433-443, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33849021

RESUMO

INTRODUCTION: Radiomics now has significant momentum in the era of precision medicine. Glioma is one of the pathologies that has been extensively evaluated by radiomics. However, this technique has not been incorporated into clinical practice. In this systematic review, we selected and reviewed the published studies about glioma grading by radiomics to evaluate this technique's feasibility and its challenges. MATERIAL AND METHODS: Using seven different search strings, we considered all published English manuscripts from 2015 to September 2020 in PubMed, Embase, and Scopus databases. After implementing the exclusion and inclusion criteria, the final papers were selected for the methodological quality assessment based on our in-house Modified Radiomics Standard Scoring (RQS) containing 43 items (minimum score of 0, maximum score of 44). Finally, we offered our opinion about the challenges and weaknesses of the selected papers. RESULTS: By our search, 1,177 manuscripts were found (485 in PubMed, 343 in Embase, and 349 in Scopus). After the implementation of inclusion and exclusion criteria, 18 papers remained for the final analysis by RQS. The total RQS score ranged from 26 (59% of maximum possible score) to 43 (97% of maximum possible score) with a mean of 33.5 (76% of maximum possible score). CONCLUSION: The current studies are promising but very heterogeneous in design with high variation in the radiomics software, the number of extracted features, the number of selected features, and machine learning models. All of the studies were retrospective in design; many are based on small datasets and/or suffer from class imbalance and lack of external validation data-sets.


Assuntos
Glioma/diagnóstico por imagem , Aprendizado de Máquina , Imageamento por Ressonância Magnética/métodos , Medicina de Precisão/métodos , Glioma/patologia , Humanos , Gradação de Tumores , Estudos Retrospectivos , Software
20.
Int J Mol Sci ; 22(9)2021 Apr 27.
Artigo em Inglês | MEDLINE | ID: covidwho-1231494

RESUMO

Artificial intelligence, or the discipline of developing computational algorithms able to perform tasks that requires human intelligence, offers the opportunity to improve our idea and delivery of precision medicine. Here, we provide an overview of artificial intelligence approaches for the analysis of large-scale RNA-sequencing datasets in cancer. We present the major solutions to disentangle inter- and intra-tumor heterogeneity of transcriptome profiles for an effective improvement of patient management. We outline the contributions of learning algorithms to the needs of cancer genomics, from identifying rare cancer subtypes to personalizing therapeutic treatments.


Assuntos
Inteligência Artificial , Neoplasias/genética , Análise de Sequência de RNA/métodos , Análise de Célula Única/métodos , Algoritmos , Biomarcadores Tumorais/genética , Humanos , Neoplasias/mortalidade , Neoplasias/patologia , Medicina de Precisão/métodos , Prognóstico , Microambiente Tumoral/genética
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