Maxi-K also known as BK channels, Slo1 or KCa1.1 channels, are one type of calcium-activated potassium channels that have large single channel conductance of 100–300 pS. Their most important physiological property is dual regulation through membrane voltage and intracellular Ca2+. 1 The complexity of this channel function mirrors the complexity of its protein structure. The amino acid sequence includes the integral membrane pore shared by all K+ channels, the integral membrane voltage sensor domains present in voltage-dependent channels, and a cytoplasmic domain (CTD) consisting of approximately 800 amino acids per subunit, which accounts for the C-terminal two thirds of the entire channel. The CTD structure confers upon the BK channel its ability to respond to changes in intracellular Ca2+. 2-5 It is also the source of functional heterogeneity through alternate splicing, polymorphisms, phosphorylation, and protein interactions, which modulate BK channel activity. 5-8 These channels modulate several physiological events, like blood pressure, smooth muscle relaxation or electrical tuning of hair cells in the cochlea and have a leading role in many pathophysiological conditions such as epilepsy, ischemic stroke, cognitive disorders, and the behavioral response to alcohol, to give only a few examples.9, 10 Studies involving activation and inactivation with pharmacological and genetic tools, including global, and tissue-specific knockouts, have implicated Maxi-K channels in cardiac function, neuroprotection, and cardio-protection from ischemia-reperfusion (IR) injury, in addition to IR-induced inflammation and mucosal barrier disruption in the small intestine. 11 It is also known that Maxi-K channels function as neuronal calcium sensors and contribute to the control of cellular excitability and the regulation of neurotransmitter release.9 Numerous Maxi-K channel blockers and activators are used to identify these channels and study their functions. Some of the most common Maxi-K channel modulators include tetraethylamonium (TEA), paxilline, penitrem A, charybdotoxin, iberiotoxin, indoles, benzimidazolones, biarylthioureas, anthraquinone analogs, tetrahydroquinolines, terpenes, benzofuroindoles, anilinoanthraquinones and quinoline. 9, 12-15 Both, the structural variety presented by the main modulators of the Maxi-K channel and the large number of pathophysiological conditions in which they are involved open a powerful research niche for the treatment of multiple pathologies.

References

1. Cui, J.; Yang, H.; Lee, U. S. Molecular mechanisms of BK channel activation. Cellular and Molecular Life Sciences. 2009, 66, 852-875.
2. Ge, L.; Hoa, N. T.; Wilson, Z.; Arismendi-Morillo, G.; Kong, X.; Tajhya, R. B.; Beeton, C.; Jadus, M. R. Big Potassium (BK) ion channels in biology, disease and possible targets for cancer immunotherapy. Int. Immunopharmacol. 2014, 22, 427-443.
3. Wallner, M.; Meera, P.; Toro, L. Determinant for β-subunit regulation in high-conductance voltage-activated and Ca2 -sensitive K channels: an additional transmembrane region at the N terminus. Proceedings of the National Academy of Sciences. 1996, 93, 14922-14927.
4. Atkinson, N. S.; Robertson, G. A.; Ganetzky, B. A component of calcium-activated potassium channels encoded by the Drosophila slo locus. Science. 1991, 253, 551-555.
5. Zang, K.; Zhang, Y.; Hu, J.; Wang, Y. The large conductance calcium-and voltage-activated potassium channel (BK) and epilepsy. CNS & Neurological Disorders-Drug Targets (Formerly Current Drug Targets-CNS & Neurological Disorders). 2018, 17, 248-254.
6. Miller, C. An overview of the potassium channel family. Genome Biol. 2000, 1, 1-5.
7. Lee, U. S.; Cui, J. BK channel activation: structural and functional insights. Trends Neurosci. 2010, 33, 415-423.
8. Currò, D. The modulation of potassium channels in the smooth muscle as a therapeutic strategy for disorders of the gastrointestinal tract. Advances in protein chemistry and structural biology. 2016, 104, 263-305.
9. Gribkoff, V. K.; Starrett Jr, J. E.; Dworetzky, S. I. Maxi-K potassium channels: form, function, and modulation of a class of endogenous regulators of intracellular calcium. Neuroscientist. 2001, 7, 166-177.
10. Hermann, A.; Sitdikova, G. F.; Weiger, T. M. Oxidative stress and maxi calcium-activated potassium (BK) channels. Biomolecules. 2015, 5, 1870-1911.
11. Goswami, S. K.; Ponnalagu, D.; Hussain, A. T.; Shah, K.; Karekar, P.; Gururaja Rao, S.; Meredith, A. L.; Khan, M.; Singh, H. Expression and activation of BKCa channels in mice protects against ischemia-reperfusion injury of isolated hearts by modulating mitochondrial function. Frontiers in cardiovascular medicine. 2019, 5, 194.
12. Hannigan, K. I.; Large, R. J.; Bradley, E.; Hollywood, M. A.; Sergeant, G. P.; McHale, N. G.; Thornbury, K. D. Effect of a novel BKCa opener on BKCa currents and contractility of the rabbit corpus cavernosum. American Journal of Physiology-Cell Physiology. 2016, 310, C284-C292.
13. Ibrahim, Z. G.; Elrewey, H. A. S. Rubidium Efflux Assay for the Determination of Calcium Activated Potassium Channel Activity. American International Journal of Biology and Life Sciences. 2020, 2, 18-27.
14. Maqoud, F.; Cetrone, M.; Mele, A.; Tricarico, D. Molecular structure and function of big calcium-activated potassium channels in skeletal muscle: pharmacological perspectives. Physiological genomics. 2017, 49, 306-317.
15. N'gouemo, P. Targeting BK (big potassium) channels in epilepsy. Expert opinion on therapeutic targets. 2011, 15, 1283-1295.

The objective of this research was to carry out a diagnosis of the working conditions in the panela industry in the Pastaza canton, Ecuador to propose a solution on the priority dangers of occupational accident. The population was made up of 208 workers from 7 panela producers in the Pastaza canton of the Tarqui and Puyo parishes, different field visits were carried out for data collection and instruments were applied such as: checklist, identification guide of Hazards and risk assessment in occupational health and safety Colombian Technical Guide ¨GTC¨ 45. In the analysis of the results, it was evidenced that the workers in this sector have a low level of education, most of them are men, with many years in activity routine and with a lack of knowledge of Occupational Safety and Occupational Health regulations and legislation, with long working hours. 40% of workers carry out heavy lifting, 100% repetitive movements, 80% of workers are exposed to psychosocial risk factors, 60% of establishments have adequate lighting, and 100% present greater risks (fire). The working conditions in the paneleras of the Pastaza canton, Ecuador, do not comply with the legislation on Occupational Safety and Health, and highly and gradually affect productivity in the microenterprise sector.

Introduction: Lactoferrin (Lf) is a whey protein with a molecular size of about 80 KDa, being found in milk and to a lesser extent in bile and tears. Lf is a cationic molecule with an isoelectric point (pI) around 8.0 to 8.5 having positive charges on its surface which is involved on its biological activity. Lf is a multifunctional protein, very important in the innate immunity system, because it can respond in various ways to physiological changes, present in several studies as preventive and therapeutic treatment, presenting antimicrobial, antifungal, immunomodulatory, antitumor, antiviral activities, as well as neutralization of bioactive substances. This work aimed to purify and characterize lactoferrin from goat milk, monitoring purification by spectroscopy techniques. Materials and methods: Skimmed milk was obtained by separating the fat from goat milk by centrifugation and subsequent acidified with HCl 0.1 M to pH 4,6 for the casein removal. The acid serum was neutralized with NaOH 0.1 M up to pH 6.8 and then centrifuged (10000 x g, 4° C, 30 min). The supernatant was submitted to saline precipitation profiles of 0-20%, 20-40%, 40-60% and 60-80% saturation of (NH₄)₂SO₄. Fluorimetric analyses salt fractions were performed under excitation length conditions at 290 nm and emission wavelengths between 300-550 nm. Results and discussions: The saline precipitation profiles of 0-20%, 20-40%, 40-60% and 60-80% saturation of (NH₄)₂SO₄ also presented the spectrum of fluorescence characteristic of lactoferrin. However, the profile of the precipitate resuspendido of 40-60% showed the spectrum of fluorescence extinction characteristic of lactoferrin with higher protein concentration. Conclusion: In the present study, it was possible to partially purify lactoferrin caprine using salin tos precipitations with saturation with (NH₄)₂SO₄ being monitored by fluorimetry.

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.

Machine learning has been proved to be useful on a pharmacological scale, too. Drug
repositioning is a very well-known method for big pharma, by which the use of an alreadyapproved drug (whose purpose is the treatment of a given disease) is extended to a different
disorder against which its efficacity has been proved.
In the most part of cases where drug response is predicted through machine learning models,
it is necessary to perform a first step where information is selected and filtered, in which
researchers must take into consideration that there is always a set of patients who will show
an extreme response, which makes almost compulsory to use a wider range of simples or cell
lines. When it comes to classifying and selecting information, three of the most used methods
are elastic net regression models, random forest ones and specifically-designed algorithms.
A training phase must always be incorporated to the process in order to callibrate the model.
Following this stage, an independent evaluation must be carried out by performing multiple
tests. This process is conducted in order to ensure that the putative model is accurate in its
predictions. Lastly, it is necesary to test the model by using clinical-resembling data.
• The evaluation can be performed via two processes: K-Fold or Leave-one-out. The first
one divides the “raw” dataset in two parts, using the first as a training dataset and the
second one as a testing dataset. Leave-one-out, however, works similarly but it leaves
only a single sample from the “raw” dataset as a test, making it compulsory to repeat
this stage many times.
• Nevertheless, general machine learning techniques can be divided in two types:
supervised machine learning, which uses already-created gruoups whithin the traning
data, or unsupervised one, which creates these groups from the trainig data.
On behalf of the building process for drug repositioning approaches, it comprises several steps,
as well. When it comes to seeking for relationships between drugs and diseases, networkbased methods can be used in any of its forms: clustering (searching relationships between
drugs and targets among clusters of these) or propagation approach. The last one can eximine
a network in a sigle region or in its entirety. Anyways, networks can include homogeneous or
heterogeneous data.
• One example of this is the Zhao and So essay, where they used several algorithms on
transcriptomic data to examine the effects on protein synthesis and expression of
various drugs and examine other potential applications for them.
Nevertheless, until today, only a few machine learning approaches have been applied on
clinical trials. This is mainly due to the difficulties that must be faced when filtering the huge
amounts of data that are used. Moreover, data-filtering procedures are sometimes not
systematic, which limits its possible uses. However, machine learning offers very interesting
benefits compared to clinical trials, as it can save researchers much time and money.
Extract from:
1. Rethinking Drug Repositioning and Development with Artificial Intelligence, Machine
Learning, and Omics [Internet]. [citado 17 de noviembre de 2021]. Disponible en:
https://www.liebertpub.com/doi/epdf/10.1089/omi.2019.0151

In this article, scientists from two Italian universities developed a model for drug delivery in order to determinate the efficacity of anticancer therapy by using Machine Learning and Artificial Neural Networks. On a more experimental level, one of the main differences of this model compared to those already existing is the generation of 3D structure models instead of 2D ones, which represent more accurately the nature of tumors.

Mathematically, they took as a basic unit the mass and volume of each phase that composes a tumor: extracelullar matrix (as an only solid phase), healthy cells, interstitial fluid and tumor cells, considering that the volume occupied by the extracelular matrix is the complementary of porosity. Firstly, in order to build the model, these researchers designed several balance equations, in each of which every addend meant the possible changes between phases considering cell growth and lysis phenomena in a tumor. It was also assumed that healthy cells mass did not change, as it was considered to be in homeostasis.

On the contrary, tumor cells were unfolded in two categories: living tumor cells and dead tumor cells, adding a new substracting factor: the death rate of tumor cells, obtaining two new equations: one expresses the mass balance for living cells (can grow or die) and the second one gives the same parameter but for necrotic cells (can appear or suffer from lysis). Adding diffusivity and considering oxigen and the drug as the nutrients (or Nt), they obtained these equations.

Latter combinations offer a last equation, of which only the last term was used as a basis for machine learning, which expresses the death rate of the drug, with λ being the rate of drug induced cell death, f being the activation function, which can have values of 0 or 1 based on the state of inactivation or activation of the drug, and ω being the mass fraction of the drug or necrotic cells. The model resulted to be accurate when predicting cell tumor growth in the abscense of drug or under low concentrations of it, but disturbing phenomena like activation time made it imposible to use the same prediction for higher amounts of drug, as drug induced cell death was not accurately calculated.

It is here where machine learning is helpful by using artificial Neural Networks in order to calculate λ as a function of drug concentration (ωdrug), tumor radius and time. In this new method, it is assumed that drug uptake did not depend on its concentration, as it was proved experimentally. As it happens sometimes when using ML, raw experimental data were not enough, so these had to be enriched by computational calculation of “more points”, giving a total of 178 training set patterns and 20 patterns for test set after many attempts until the prediction of drug death rate λ was accurate enough. The best topology for the Neural Network was of three layers of 3, 2 and 2 nodes, respectively. In this terms, ANN was very accurate when testing and predicted a λ of .

This experiment proves once again the necessity of improving ML models, as they are not accurate enough with a low number of training samples, but it gives promising results for its applications.

1. Boso DP, Di Mascolo D, Santagiuliana R, Decuzzi P, Schrefler BA. Drug delivery: Experiments, mathematical modelling and machine learning. Comput Biol Med. agosto de 2020;123:103820.

Last year, Burdick et al. Performed a clinical essay in which they assesed the possibility of
predicting COVID-19 patients’ possibility of needing mechanical ventilation according to their
The machine learning model that was used in this study is based on Gradient Boosting, which
consists on combinating multiple decision tres in order to créate prediction scores. In these
trees, patients are split in smaller groups following whether or not they present the features
that are sequentially demanded, with the consequence that new groups are smaller and smaller.
Datasets used for training were different to those used for testing. This is an important point, as
it is necessary to ensure that all data used is comparable. The algorithm developed was
compared with MEWS, a health index based on body temperature, respiratory and heart rates,
etc., which is useful to predict the future need for increased medical attention. On the other
hand, the machine learning algorithm was built following these same values (as well as some
other ones that were available).
The results of the algorithm built by machine learning were promising: it had a better sensitivity
and specificity than MEWS when it came to predicting ventilation necessities in this group of
patients, with its predicting capability being a 16% better than MEWS’.
All things considered, it is possible to conclude that the general use of this models offers a path
to reduce false negatives and false positives. One possible problem is the lack of some values,
as they were taken from real patients, but they are not considered to affect the outcome, as
researchers state these lacks may have been due to the fact that missing datasets were not
important and thus they were not worth measuring. However, there are two limitations that
must be taken into account: the sample used was small (only a small fraction of the total patients
did require artificial ventilation) and was only composed by COVID-19 patients, so the model
might not be as accurate when being applied to other disorders that require assisted ventilation,
so this is another example of ML limitations in some circumstances where data is not abundant.
1. Burdick H, Lam C, Mataraso S, Siefkas A, Braden G, Dellinger RP, et al. Prediction of
respiratory decompensation in Covid-19 patients using machine learning: The READY trial.
Comput Biol Med. septiembre de 2020;124:103949.

The study of the Metabolic Networks (MNs) of those bacteria with high resistance to Nanoparticles (NPs) action may give clues for the future design of new NP with specific antibacterial activity. In our previous work, we reported that the values of p(f(n,c,j,s)=1)_pred are the probabilities with which a given NP is predicted to be active against the bacteria with a given MN_s. We can interpret these probabilities as a measure of bacterial susceptibility to NPs. Consequently, from the point of view of the MNs those bacteria with low values of p(f(n,c,j,s)=1)_pred are predicted to be very resistant to the action of the n^th NP in the j^th assay. Accordingly, a low average value p(f(n,c,j,s)=1)_avg = <p(f(n,c,j,s)=1)_pred> (average of all p(f(n,c,j,s)=1)_pred values) for the s^th bacteria vs. the same NP in different assays indicates that this specie should be very resistant to this NP in particular regardless the assay selected. In order to compare the structure of the MNs of different bacteria vs. the predicted p(f(n,c,j,s)=1)_avg we could use a single numerical parameter of MN metabolic structure (network topology). In this work we used 3 numerical parameters related to MNs metabolic structure, N_ms, <L_ins>, and <L_outs>. We used these parameters to calculate a unique parameter that fusion all this information. We are going to call this parameter as the Anabolism-Catabolism Unbalance (ACU_s) index of MN_s of the s^th bacteria specie. In this communication we discussed the use of this index. Full publication: Nanoscale. 2021 Jan 21;13(2):1318-1330. doi: 10.1039/d0nr07588d.

The occurrence of the phenomenon, where loss of an adequate response to anti-cancer treatment is observed, or drug resistance, is connected with, among other things: the occurrence of new DNA mutations; metabolic changes in cancer cells; drug inactivation; inhibition of cancer cell apoptosis; the epithelial-mesenchymal transition (EMT); heterogeneity of the cells constituting the tumor mass; the influence of epigenetic factors; as well as any combination of the listed factors.

The main aim of this study was to determine the expression profile of mRNA and miRNA related with the drug resistance phenomenon in Ishikawa line endometrial cancer cells treated with salinomycin compared to the control culture.Ishikawa line endometrial cancer cells were exposed to salinomycin at a concentration of 1µM over a period of 12,24 and 48 hours, compared to a control culture, which was formed of cells untreated by the drug.Assessing the microarray expression profile of genes related to drug resistance, it was observed that the number of mRNA differentiating the culture incubated with the drug from the control, depending on the exposition time of the cells to salinomycin was the following: H_12 vs C – 9 mRNA; H_24 vs C - 7 mRNA; H_48 vs C - 1 mRNA. The largest changes in gene expression were determined for: TUFT1; ABCB1; MTMR11; SLC30A5.

Based on the conducted research as part of this study, it was confirmed that salinomycin added to the Ishikawa line endometrial cancer cell culture indices changes in the mRNA and miRNA transcriptome related to the drug resistance phenomenon and caspase pathway. Furthermore, we observed that salinomycin induces apoptosis in endometrial cancer cells, mainly through the mitochondrial pathway.

Endometrial cancer is the most common gynecological tumor in peri- and postmenopausal women. The best known and best-described adipokines are: leptin, adiponectin, visfatin, resistin, and omentin.

Leptin (LEP) is primarily secreted by differentiated adipocytes, influencing neoplastic angiogenesis through the activation of the JAK/STAT pathway, which results in the increased proliferation of vascular endothelial cells as well as an increased expression of the vascular endothelial growth factor (VEGF), fibroblast growth factor (FGF) and anti-apoptotic proteins, namely Bcl-2. In women whose BMI is higher than 25, the risk of endometrial cancer is doubled and in those whose BMI is above 30, this risk is tripled. A higher concentration of LEP in the serum of women with endometrial cancer compared to healthy volunteers was also confirmed.

In turn, adiponectin (ADP) demonstrates an insulin-sensitizing effect, increasing the expression of nitric oxide synthase, furthermore, it also has anti-inflammatory activity through inhibiting the expression of tumor necrosis factor-alpha (TNF-α) and interleukin 6 (IL-6). A 6-times higher risk of cancer development is noted in obese people, in whom the concentration of ADP was lower than in the group of healthy volunteers. It is highlighted that it is this adipokine that is most strongly associated with the risk of endometrial cancer.

The overriding aim of this study was the assessment of changes in the expression pattern of genes coding chosen adipokines

in an Ishikawa line endometrial cancer cell culture exposed to the effects of cisplatin, compared to a control culture.

Ishikawa line endometrial cancer cells were exposed to the effects of cisplatin at concentrations of 2.5 µM, 5 µM, and 10 µM for 12, 24, and 48 hours, and afterward compared to a control culture (C), which consisted of cells untreated using cisplatin. For each exposition period and cisplatin concentration, 3 technical repetitions were conducted.

From the results of the microarray experiment, it can be observed that under the influence of cisplatin, there is a decrease in the transcriptional activity of leptin and its’ three receptors. A lower expression of the discussed genes was noted, the higher the concentration of the drug added to the culture and the higher the exposition time of the cells to the drug was (p<0.05).

In turn, for adiponectin and its’ receptors, an opposite expression profile to leptin and its’ receptors is noted, depending on the cisplatin concentration and treatment time of the endometrial cancer cells using it. The higher the drug concentration and cell exposition time, the higher the overexpression of adiponectin and its’ receptors.

Changes in the expression profiles of leptin, adiponectin, can be explored in the context of utilizing them as supplementary diagnostic markers of endometrial cancer, monitoring the effectiveness of cisplatin therapy, and in the creation of new therapeutic strategies aimed toward the JAK/STAT signaling pathway.