Deaths from sepsis are the most common cause of death and rank among the leading causes of death. In these diseases caused by bacteria, the time for the detection of the bacteria causing the problems and their immediate treatment is important so as not to produce serious effects of sepsis and to avoid septic shock. But conventional methods such as blood culture, biochemical identification, immunological testing or PCR amplification only give us long-term resolution of a small fraction of bacteria.
According to the latest published studies on the metabolomic changes produced by infecting microorganisms in patients, they produce a type of biomarkers or biosignatures that are produced when these pathogens proliferate and grow. Trials have shown that using specific metabolomic biosignatures of plasma pathogens, these pathogens that cause sepsis could be detected in a much faster and more efficient way. The big problem with this novel idea is the small number of samples that are registered against the large number of characteristics that occur in different sepsis infections.
The solution of using the machine learning model has been seen to this problem, which could integrate a large amount of clinical information and through the use of predictive algorithms could give us a greater identification of pathogens.
A retrospective cohort study of some clinical cases of sepsis has been carried out, where the information of the 1152 patients with signs of the disease has been collected and 100 individuals with a resolved clinical case have been chosen and compared with 29 controls. Both Gram-negative and Gram-positive organisms have been analyzed, among them the most prominent Streptococcus pneumoniae, Staphylococcus aureus and Escherichia coli. The variables of the studies have been used the sensitivity, the specificity and the value of AUC of the clinical and metabolomic characteristics in the prediction of the diagnostic results.

We can conclude that the studies analysed by the machine learning model gave us abnormal nitrogen metabolism results, cellular respiratory disorders, and kidney or intestinal failure. These data are important in order to detect sepsis and discriminate against other possible common causes. This advance in septic diseases could help us to identify the causative pathogens in a much greater way and try to take advantage of more time in treating the disease. We can highlight the great advantage of the machine learning model of great information management and a great speed in searching for a common factor to a problem for its greater resolution and detection.

Machine learning is something relatively new in medicine. As it can be seen on Pubmed, the vast
majority of articles that have been published regarding this method are not older than ten years.
This article gives a brief explanation of current trends and unsolved problems in the field of
machine learning applied to medicine. Firstly, it is necessary to highlight the difference between
supervised machine learning and unsupervised one. The first allows the performance of
predictions of a known target, as it is the case of the Framingham Risk Score, an algorythm by
which it is posible to predict the probability of a Cardiovascular Heart Disease in a more or less
reliably way. On the other hand, unsupervised machine learning only allows the user to identify
patterns in a dataset, which might be helpful as approaches to therapy.
As it is stated in Deo et al.’s article, machine learning in medicine has been poorly studied
previously in part due to the fact that, in order to obtain significant information (e.g., when
predicting a disease), it is necessary to classify a set of features according to their general degree
of involvement in the disorder and rejecting those who do not seem significant, but these ones
might be relevant only in certain subgroups of patients. It is also necessary to find a model
flexible enough to minimize these effects.
Moreover, it is important to understand that machine comprises two phases: a training one,
performed via a set of examples with the aim of fit the parameters in the model, and a test, to
evaluate the calibrated model. In any case, it is essential to ensure that the input information
includes every single feature needed for the model.
Anyway, a complex model might not be the best option when few training samples are available
(as its generalization capacities are not adequate) or when the relationship beetweeen the
features studied and the expected result is simple.
One example of what has previously been explained is C-path, a very interesting approach
obtained by using machine learning in the Stanford University, where this method was a key tool
in the calculation of breast cancer possibilities, as this model is based on 6642 tissular predictors
through image processing, which means this enabled researchers to know a patient’s breast
cancer possibilities from an image of this patient’s interior by using computer technology to spot
concrete features on affected tissues. Despite being a simple model, it had satisfactory results.
1. Deo RC. Machine Learning in Medicine. Circulation. 17 de noviembre de
2015;132(20):1920-30.

HIV-1 protease, a homodimer, has attracted the interest of many researchers due to its essential role in HIV replication and subsequent functional activities. It hydrolyses different viral proteins into their functional form to help in maturing the virus for further extending the disease propagation. The present workflow in this research was designed to identify potential HIV-1 protease inhibitors from library of approved drugs (1,428 compounds) using computational techniques in Computer-Aided-Drug-Design (CADD). The inhibitory potency of the dataset was evaluated using the lowest theoretical binding energies of the target-ligand complex. Software and tools such as Molecular Operating Environment (MOE), AutoDock Vina, Discovery Studio used in CADD were employed during the process of this work. This study suggest the possibility of repurposing some current drugs (from the library) to having potential HIV Protease inhibitory effect.

Chitosan is a linear amino-polysaccharide of β(1→4)-linked D-glucosamine and N-acetyl-D-glucosamine residues. Also called poly-β-(1-4)-2-amino-2-deoxy-β-D-glucose, it is a natural heteropolymer obtained by alkaline deacetylation of the amino acetyl groups of chitin, which is the main component of the exoskeleton of insects, crabs, shrimp, and krill. The design and fabrication of chitosan-based hydrogels is based on the association with glycerophosphate salt. The mixture remains liquid at room temperature, and it gels quickly when heated to body temperature.

This work, in the form of patent analysis, presents the state by introducing what has been innovated and patented concerning chitosan-based hydrogels. Furthermore, a detailed analysis of the patentability, by using the “chemical compounds search” of the PATENTSCOPE database, has been provided regarding publication years, inventors, applicants, owners, jurisdictions, and classifications.

Phelan-McDermid Syndrome (PMS) is a rare genetic condition caused by a deletion of the terminal end of chromosome 22 in the 13.3 region, as well as, by point mutations within SHANK3 gene. The most characteristic clinical symptom is global developmental delay, absent or severely delayed speech, hypotonia and autism spectrum disorder. The syndrome is underdiagnosed and its real incidence remains unknown, but more than 2,000 cases have been reported worldwide. In the present investigation patients diagnosed with PMS for twelve years were recruited throughout the Spanish territory. The clinical patient information was obtained from the referral doctors using two standardized questionnaires completed with data from the medical reports and the interview with the parents. The molecular diagnosis of the disease was carried out using different formats of microarrays. Data were processed using Microsoft Excel and Statgraphics Centurion XVII.

Currently, there are 201 people diagnosed with PMS in Spain with a prevalence of 4x10-4/10,000. The community with the most diagnosed patients is Madrid and there are no significant differences in terms of sex. The mean age at diagnosis is around 6.67 years. Therefore, the prevalence of the disease in Spain is very low, and it can be stated that it is very likely that there are more people with this syndrome in the population.