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

http://www.healthcare.uiowa.edu/corefacilities/esr/publications/buettnerpubs/pdf/FRBM-2001-30-1191-FQS-Redox.pdf

Redox environment of the cell as viewed through the redox state of the glutathione disulfide/glutathione couple.

Pathology and genetics of tumors of soft tissue and bone

In this paper we extend the “network theory of aging,” and we argue that a global reduction in the capacity to cope with a variety of stressors and a concomitant progressive increase in proinflammatory status are major characteristics of the aging process. This phenomenon, which we will refer to as “inflamm-aging,” is provoked by a continuous antigenic load and stress. On the basis of evolutionary studies, we also argue that the immune and the stress responses are equivalent and that antigens are nothing other than particular types of stressors. We also propose to return macrophage to its rightful place as central actor not only in the inflammatory response and immunity, but also in the stress response. The rate of reaching the threshold of proinflammatory status over which diseases/disabilities ensue and the individual capacity to cope with and adapt to stressors are assumed to be complex traits with a genetic component. Finally, we argue that the persistence of inflammatory stimuli over time represents the biologic background (first hit) favoring the susceptibility to age-related diseases/disabilities. A second hit (absence of robust gene variants and/or presence of frail gene variants) is likely necessary to develop overt organ-specific age-related diseases having an inflammatory pathogenesis, such as atherosclerosis, Alzheimer's disease, osteoporosis, and diabetes. Following this perspective, several paradoxes of healthy centenarians (increase of plasma levels of inflammatory cytokines, acute phase proteins, and coagulation factors) are illustrated and explained. In conclusion, the beneficial effects of inflammation devoted to the neutralization of dangerous/harmful agents early in life and in adulthood become detrimental late in life in a period largely not foreseen by evolution, according to the antagonistic pleiotropy theory of aging.

Inflamm‐aging: An Evolutionary Perspective on Immunosenescence

In the last years, the scientific community has been pushed by an increasing public concern to search for correct and comprehensive answers to problem related to the possible harmful effects of electromagnetic fields generated by power lines and domestic appliances. Such a problem can be inscribed in that, most general, concerning the biological effects of non ionizing radiations, and in particular the effects the extremely low frequency (ELF) electromagnetic fields (EMF). A consistent number of papers have demonstrated that the in vitro exposure of immune cells to nonthermal ELF-EMF can elicit molecular and cellular changes that might be relevant to the activity of the immune system in vivo. Nonionizing electromagnetic radiations with the same physical characteristics can also affect cells of the neuroendocrine and musculoskeletal systems. Notwithstanding the great amount of information that are now available, the underlying mechanisms by which such fields can induce cellular changes are poorly known. For example, it has not been clearly established the single contribution of electric or magnetic fields. In any case, it is known that a variety of intracellular molecules and structures are relevant targets for the biological actions of ELF-EMF. Indeed, effects have been reported on the synthesis of DNA, RNA, and proteins; cell proliferation; cation fluxes and binding; production of mediators shared by the immune, nervous and endocrine systems such as cytokines; and membrane signal transduction (i.e., hormones, enzymes, and neuro-transmitters), among others. In most cases, such effects occurred as a result of short-term exposure of cells to ELF-EMF of extreme low intensities.

Immunological Effects of ELF Electric and Magnetic Fields: An Overview

In old people, many alterations of innate and adaptive immunity have been described and viewed as deleterious, hence the term immunosenescence. Immunosenescence is a complex process involving multiple reorganizational and developmentally regulated changes, rather than a simple unidirectional decline of the whole function. Whereas innate immunity is relatively well preserved in older people, adaptive immunity is more susceptible due both to the functional decline associated with the passage of time and to antigen burden to which an individual has been exposed during lifetime. Although it is conceivable that global immune changes may lead to various diseases, it is also obvious that these changes may be needed for extended survival/longevity. This chapter summarizes recent data on the dynamic reassessment of immune changes with aging.

Aging of immune system

Small RNAs in prokaryotes and eukaryotes: a lesson for human immunologists: comment on "Diversity, evolution, and therapeutic applications of small RNAs in prokaryotic and eukaryotic immune systems" by Edwin L. Cooper and Nicola Overstreet.

BACKGROUND In patients undergoing orthotopic liver transplant (OLT), immunosuppressive treatment is mandatory and infections are leading causes of morbidity/mortality. Thus, it is essential to understand the functionality of cell-mediated immunity after OLT. The aim of the study was to identify changes in T-cell phenotype and polyfunctionality in human immunodeficiency virus-positive (HIV+) and -negative (HIV-) patients undergoing immunosuppressive treatment after OLT. METHODS We studied peripheral blood mononuclear cells from 108 subjects divided into 4 groups of 27: HIV+ transplanted patients, HIV- transplanted patients, HIV+ nontransplanted patients, and healthy subjects. T-cell activation, differentiation, and cytokine production were analyzed by flow cytometry. RESULTS Median age was 55 years (interquartile range, 52-59 years); the median CD4 count in HIV+ patients was 567 cells/mL, and all had undetectable viral load. CD4+ and CD8+ T-cell subpopulations showed different distributions between HIV+ and HIV- OLT patients. A cluster representing effector cells expressing PD1 was abundant in HIV- transplanted patients and they were characterized by higher levels of CD4+ T cells able to produce interferon-γ and tumor necrosis factor-α. CONCLUSIONS HIV- transplanted patients have more exhausted or inflammatory T cells compared to HIV+ transplanted patients, suggesting that patients who have already experienced a form of immunosuppression due to HIV infection respond differently to anti-rejection therapy.

Efficient T-Cell Compartment in HIV-Positive Patients Receiving Orthotopic Liver Transplant and Immunosuppressive Therapy.

https://link-springer-com-443.webvpn.jmu.edu.cn/content/pdf/10.1007%2F978-3-319-99375-1_122.pdf

Andrea Cossarizza

Papers

Immunological Effects of ELF Electric and Magnetic Fields: An Overview

Aging of immune system

Small RNAs in prokaryotes and eukaryotes: a lesson for human immunologists: comment on "Diversity, evolution, and therapeutic applications of small RNAs in prokaryotic and eukaryotic immune systems" by Edwin L. Cooper and Nicola Overstreet.

Efficient T-Cell Compartment in HIV-Positive Patients Receiving Orthotopic Liver Transplant and Immunosuppressive Therapy.

Circulating Mitochondrial DNA as a Potential Biomarker for Aging and Its Related Complications