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

Objective:To provide an update to the “Surviving Sepsis Campaign Guidelines for Management of Severe Sepsis and Septic Shock,” last published in 2008.Design:A consensus committee of 68 international experts representing 30 international organizations was convened. Nominal groups were assembled at ke

Surviving sepsis campaign: international guidelines for management of severe sepsis and septic shock: 2012.

The capacity to predict future events permits a creature to detect, model, and manipulate the causal structure of its interactions with its environment. Behavioral experiments suggest that learning is driven by changes in the expectations about future salient events such as rewards and punishments. Physiological work has recently complemented these studies by identifying dopaminergic neurons in the primate whose fluctuating output apparently signals changes or errors in the predictions of future salient and rewarding events. Taken together, these findings can be understood through quantitative theories of adaptive optimizing control.

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A Neural Substrate of Prediction and Reward

To provide an update to the “Surviving Sepsis Campaign Guidelines for Management of Severe Sepsis and Septic Shock,” last published in 2008. A consensus committee of 68 international experts representing 30 international organizations was convened. Nominal groups were assembled at key international meetings (for those committee members attending the conference). A formal conflict of interest policy was developed at the onset of the process and enforced throughout. The entire guidelines process was conducted independent of any industry funding. A stand-alone meeting was held for all subgroup heads, co- and vice-chairs, and selected individuals. Teleconferences and electronic-based discussion among subgroups and among the entire committee served as an integral part of the development. The authors were advised to follow the principles of the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system to guide assessment of quality of evidence from high (A) to very low (D) and to determine the strength of recommendations as strong (1) or weak (2). The potential drawbacks of making strong recommendations in the presence of low-quality evidence were emphasized. Recommendations were classified into three groups: (1) those directly targeting severe sepsis; (2) those targeting general care of the critically ill patient and considered high priority in severe sepsis; and (3) pediatric considerations. Key recommendations and suggestions, listed by category, include: early quantitative resuscitation of the septic patient during the first 6 h after recognition (1C); blood cultures before antibiotic therapy (1C); imaging studies performed promptly to confirm a potential source of infection (UG); administration of broad-spectrum antimicrobials therapy within 1 h of the recognition of septic shock (1B) and severe sepsis without septic shock (1C) as the goal of therapy; reassessment of antimicrobial therapy daily for de-escalation, when appropriate (1B); infection source control with attention to the balance of risks and benefits of the chosen method within 12 h of diagnosis (1C); initial fluid resuscitation with crystalloid (1B) and consideration of the addition of albumin in patients who continue to require substantial amounts of crystalloid to maintain adequate mean arterial pressure (2C) and the avoidance of hetastarch formulations (1B); initial fluid challenge in patients with sepsis-induced tissue hypoperfusion and suspicion of hypovolemia to achieve a minimum of 30 mL/kg of crystalloids (more rapid administration and greater amounts of fluid may be needed in some patients (1C); fluid challenge technique continued as long as hemodynamic improvement is based on either dynamic or static variables (UG); norepinephrine as the first-choice vasopressor to maintain mean arterial pressure ≥65 mmHg (1B); epinephrine when an additional agent is needed to maintain adequate blood pressure (2B); vasopressin (0.03 U/min) can be added to norepinephrine to either raise mean arterial pressure to target or to decrease norepinephrine dose but should not be used as the initial vasopressor (UG); dopamine is not recommended except in highly selected circumstances (2C); dobutamine infusion administered or added to vasopressor in the presence of (a) myocardial dysfunction as suggested by elevated cardiac filling pressures and low cardiac output, or (b) ongoing signs of hypoperfusion despite achieving adequate intravascular volume and adequate mean arterial pressure (1C); avoiding use of intravenous hydrocortisone in adult septic shock patients if adequate fluid resuscitation and vasopressor therapy are able to restore hemodynamic stability (2C); hemoglobin target of 7–9 g/dL in the absence of tissue hypoperfusion, ischemic coronary artery disease, or acute hemorrhage (1B); low tidal volume (1A) and limitation of inspiratory plateau pressure (1B) for acute respiratory distress syndrome (ARDS); application of at least a minimal amount of positive end-expiratory pressure (PEEP) in ARDS (1B); higher rather than lower level of PEEP for patients with sepsis-induced moderate or severe ARDS (2C); recruitment maneuvers in sepsis patients with severe refractory hypoxemia due to ARDS (2C); prone positioning in sepsis-induced ARDS patients with a Pao
 2/Fio
 2 ratio of ≤100 mm Hg in facilities that have experience with such practices (2C); head-of-bed elevation in mechanically ventilated patients unless contraindicated (1B); a conservative fluid strategy for patients with established ARDS who do not have evidence of tissue hypoperfusion (1C); protocols for weaning and sedation (1A); minimizing use of either intermittent bolus sedation or continuous infusion sedation targeting specific titration endpoints (1B); avoidance of neuromuscular blockers if possible in the septic patient without ARDS (1C); a short course of neuromuscular blocker (no longer than 48 h) for patients with early ARDS and a Pao
 2/Fi
 o
 2 180 mg/dL, targeting an upper blood glucose ≤180 mg/dL (1A); equivalency of continuous veno-venous hemofiltration or intermittent hemodialysis (2B); prophylaxis for deep vein thrombosis (1B); use of stress ulcer prophylaxis to prevent upper gastrointestinal bleeding in patients with bleeding risk factors (1B); oral or enteral (if necessary) feedings, as tolerated, rather than either complete fasting or provision of only intravenous glucose within the first 48 h after a diagnosis of severe sepsis/septic shock (2C); and addressing goals of care, including treatment plans and end-of-life planning (as appropriate) (1B), as early as feasible, but within 72 h of intensive care unit admission (2C). Recommendations specific to pediatric severe sepsis include: therapy with face mask oxygen, high flow nasal cannula oxygen, or nasopharyngeal continuous PEEP in the presence of respiratory distress and hypoxemia (2C), use of physical examination therapeutic endpoints such as capillary refill (2C); for septic shock associated with hypovolemia, the use of crystalloids or albumin to deliver a bolus of 20 mL/kg of crystalloids (or albumin equivalent) over 5–10 min (2C); more common use of inotropes and vasodilators for low cardiac output septic shock associated with elevated systemic vascular resistance (2C); and use of hydrocortisone only in children with suspected or proven “absolute”’ adrenal insufficiency (2C). Strong agreement existed among a large cohort of international experts regarding many level 1 recommendations for the best care of patients with severe sepsis. Although a significant number of aspects of care have relatively weak support, evidence-based recommendations regarding the acute management of sepsis and septic shock are the foundation of improved outcomes for this important group of critically ill patients.

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Surviving Sepsis Campaign: International Guidelines for Management of Severe Sepsis and Septic Shock, 2012

The transdifferentiation of epithelial cells into motile mesenchymal cells, a process known as epithelial-mesenchymal transition (EMT), is integral in development, wound healing and stem cell behaviour, and contributes pathologically to fibrosis and cancer progression. This switch in cell differentiation and behaviour is mediated by key transcription factors, including SNAIL, zinc-finger E-box-binding (ZEB) and basic helix-loop-helix transcription factors, the functions of which are finely regulated at the transcriptional, translational and post-translational levels. The reprogramming of gene expression during EMT, as well as non-transcriptional changes, are initiated and controlled by signalling pathways that respond to extracellular cues. Among these, transforming growth factor-β (TGFβ) family signalling has a predominant role; however, the convergence of signalling pathways is essential for EMT.

Molecular mechanisms of epithelial–mesenchymal transition

Background: In recent years proteomic approaches have been widely used to diagnose disease and the new technology of surface enhanced laser-desorption and ionization time-of-flight mass spectrometry (SELDI-TOF MS) is very promising. Methods: A review of english language literature was undertaken using internet databases such as PubMed and Medline for studies using proteomic technologies for the early detection of cancer. Results: SELDI-TOF is an array based mass spectrometric method in which proteins of interest are selectively absorbed onto a chemically modified surface and the mass and amount of each protein is measured by irradiating the surface with a laser and measuring the time-of-flight. The technology has revealed a large number of previously uncharacterized biomarkers for a wide variety of cancers. Its versatility has also been demonstrated by its application as a tool for mining a wide variety of biological tissues and fluids. Conclusions: The majority of these studies have discriminated between diseased and healthy controls with a high degree of sensitivity and specif city. The reproducibility, standardization and feasibility of this technology need to be addressed before these proteomic approaches to the discovery of novel, highly sensitive diagnostic tools can become routine clinical care

SELDI-TOF proteomic analysis and cancer detection

Despite serious reservations expressed during consultation, the National Institute for Health and Care Excellence (NICE) has recently issued referral guidance for suspected colorectal cancer in which faecal occult blood testing (FOBT) is recommended for certain low risk symptomatic patients.1 2 We believe that this will lead to false reassurance and delayed investigations. We should like to point out that:

Use of faecal occult blood tests in symptomatic patients

Epigenetic mechanisms in carcinogenesis may have a significant role in the development of colorectal cancer. To investigate this phenomenon in early-stage disease, promoter methylation status in the tumour suppressor genes APC, MGMT, hMLH1, P14/P14ARF, and CDKN2A/P16 was investigated in 78 colorectal adenomas. These had previously been characterized for mutations of APC, KRAS, and TP53 genes and for chromosomal abnormality by comparative genomic hybridization (CGH). APC hypermethylation was seen in 52 tumours (66.7%). APC showed either methylation or mutation in 66 lesions (84.6%), but these events were not statistically associated. MGMT methylation was detected in 39 cases (50%). Adenomas with this abnormality showed a significantly lower number of chromosomal changes by CGH (p < 0.02), confirming that DNA repair defect of this type is associated with a lower level of chromosomal instability. An hMLH1 methylation defect was seen in only one adenoma (1.3%), from a patient who had a synchronous cancer showing the same defect. Methylation of P14 (P14ARF) was seen in 31 adenomas (39.7%) and CDKN2A (P16) abnormality in 25 (32.1%). DNA methylation at two or more loci was seen in 46 tumours (59%), while 11 lesions (14.1%) showed no evidence of hypermethylation at any of the loci studied. Methylation at any or all of MGMT, P14 or P16 was significantly associated with APC methylation (p = 0.01). Those neoplasms with more than two methylated genes showed significantly fewer chromosomal abnormalities than adenomas with one or no methylated loci (p < 0.001). There was no association between specific individual chromosomal abnormalities, APC, KRAS or TP53 mutations and any pattern of methylation abnormality. We conclude that methylation abnormality is very common in pre-invasive colorectal neoplasia, and that high level methylation is associated with low level chromosomal instability.

Robert Steele

Papers

SELDI-TOF proteomic analysis and cancer detection

Use of faecal occult blood tests in symptomatic patients

Relationship between point gene mutation, chromosomal abnormality, and tumour suppressor gene methylation status in colorectal adenomas.

SUSPECTED CANCER IN ADULTS Use of faecal occult blood tests in symptomatic patients

A phase II study of the oral matrix metalloproteinase inhibitor, marimastat, in patients with inoperable gastric cancer