Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

COVID-19 Dashboard

Treatment is complex for patients with head and neck (H&N) cancers with specific site of disease, stage, and pathologic findings guiding treatment decision-making. Treatment planning for H&N cancers involves a multidisciplinary team of experts. This article describes supportive care recommendations in the NCCN Guidelines for Head and Neck Cancers, as well as the rationale supporting a new section on imaging recommendations for patients with H&N cancers. This article also describes updates to treatment recommendations for patients with very advanced H&N cancers and salivary gland tumors, specifically systemic therapy recommendations.

/pdf/head-and-neck-cancers-version-2-2020-nccn-clinical-practice-2uiqez5t2e.pdf

Head and Neck Cancers, Version 2.2020, NCCN Clinical Practice Guidelines in Oncology.

Glioblastomas are the most common form of malignant primary brain tumor and an important cause of morbidity and mortality. In recent years there have been important advances in understanding the molecular pathogenesis and biology of these tumors, but this has not translated into significantly improved outcomes for patients. In this consensus review from the Society for Neuro-Oncology (SNO) and the European Association of Neuro-Oncology (EANO), the current management of isocitrate dehydrogenase wildtype (IDHwt) glioblastomas will be discussed. In addition, novel therapies such as targeted molecular therapies, agents targeting DNA damage response and metabolism, immunotherapies, and viral therapies will be reviewed, as well as the current challenges and future directions for research.

/pdf/glioblastoma-in-adults-a-society-for-neuro-oncology-sno-and-401m4809du.pdf

Glioblastoma in adults: a Society for Neuro-Oncology (SNO) and European Society of Neuro-Oncology (EANO) consensus review on current management and future directions

A PPAR gamma agonist protects against oral mucositis induced by irradiation in a murine model

Radiotherapy remains a cornerstone in metastatic cancer treatment but is often hindered by tumor hypoxia and radioresistance. Gold nanorods (AuNRs) offer promise in enhancing radiotherapy through hyperthermia, yet their clinical impact is limited by poor tumor targeting. Building on the previous findings demonstrating the tumor‐homing ability of Endothelial Colony Forming Cells (ECFCs) loaded with AuNRs, this study advances their use as a biologically targeted delivery system for precise radiotherapy enhancement. Using 3D in vitro tumor models and in vivo studies with nude rats, it is demonstrated that ECFCs actively home to hypoxic tumor regions, overcoming traditional nanoparticle delivery limitations. This targeted approach ensures efficient AuNR accumulation, enhancing photothermal activation and maximizing radiosensitization. In vitro, ECFC‐loaded AuNRs significantly amplify radiotherapy effects, inducing ferroptosis in melanoma and inhibiting autophagy in breast cancer cells—revealing distinct tumor‐specific mechanisms. Moreover, ECFC‐AuNRs suppress tumor proliferation and angiogenesis, blocking vessel‐like structure formation in vitro and in vivo. By integrating cellular therapy with nanotechnology, this study presents a novel strategy to counter radioresistance and improve therapeutic precision. These findings lay the foundation for a clinically viable, patient‐specific approach, unlocking new possibilities in advanced cancer treatment.

Antitumoral Efficacy of AuNRs-Laden ECFCs In Vitro and In Vivo: Decoding the Heat and Rays Combo Treatment in Breast Cancer and Melanoma Cells.

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A PPAR gamma agonist protects against oral mucositis induced by irradiation in a murine model

Antitumoral Efficacy of AuNRs-Laden ECFCs In Vitro and In Vivo: Decoding the Heat and Rays Combo Treatment in Breast Cancer and Melanoma Cells.