Immunological aspects of probiotics for improving skin diseases: Influence on the Gut-Brain-Skin Axis.

The interplay between gut microbiota and human health, both mental and physical, is well-documented. This connection extends to the gut-brain-skin axis, linking gut microbiota to skin health. Recent studies have underscored the potential of probiotics and prebiotics to modulate gut microbiota, supported by in vivo and clinical investigations. In this comprehensive review, we explore the immunological implications of probiotics in influencing the gut-skin axis for the treatment and prevention of skin conditions, including psoriasis, acne, diabetic ulcers, atopic dermatitis, and skin cancer. Our analysis reveals that probiotics exert their effects by modulating cytokine production, whether administered orally or topically. Probiotics bolster skin defenses through the production of antimicrobial peptides and the induction of keratinocyte differentiation and regeneration. Yet, many questions surrounding probiotics remain unanswered, necessitating further exploration of their mechanisms of action in the context of skin diseases.

Comparison of culture and PCR-DGGE methods to evaluate the airways of cystic fibrosis patients and determination of their antibiotic resistance profile.

Background and Objectives: Respiratory infections are the most serious condition in cystic fibrosis (CF) patients; therefore, a thorough comprehension of the diversity and dominant microbial species in CF airways has a crucial role in treatment. Our objective was to determine the antibiotic resistance profile of CF airways microbiota and compare culture methods and PCR-DGGE to evaluate bacterial diversity. Materials and Methods: Pharyngeal swabs from 121 CF patients were collected. The samples were then cultured, identified and antibiotic resistance testing was performed. Thirty samples were subjected to further molecular surveys. DNA contents of these samples were extracted and amplified using nested-PCR technique and their bacterial diversity was assessed by DGGE. The DGGE patterns were visualized and certain bands were excised and purified. Next, the DNA was amplified by another round of PCR and sent out for sequencing. Results: Staphylococcus aureus, Pseudomonas aeruginosa, and Klebsiella pneumoniae were the most prevalent species isolated using culture methods. S. aureus was the most common bacteria among 6 years and younger patients; while, P. aeruginosa had more prevalence among older ones. The PCR-DGGE results showed more diversity than culture methods, particularly in younger patients who exhibited more bacterial diversity than the older groups. Sequencing results unveiled the presence of certain bacterial species including Haemophilus parainfluenzae and Stenotrophomonas maltophilia which were completely missed in culture. Conclusion: Even though culture-dependent methods are cost-effective, PCR-DGGE appeared to be more efficient to determine bacterial diversity. PCR-DGGE detects less abundant species, though their viability could not be determined using this method.

Single-Cell Measurements and Modeling and Computation of Decision-Making Errors in a Molecular Signaling System with Two Output Molecules.

A cell constantly receives signals and takes different fates accordingly. Given the uncertainty rendered by signal transduction noise, a cell may incorrectly perceive these signals. It may mistakenly behave as if there is a signal, although there is none, or may miss the presence of a signal that actually exists. In this paper, we consider a signaling system with two outputs, and introduce and develop methods to model and compute key cell decision-making parameters based on the two outputs and in response to the input signal. In the considered system, the tumor necrosis factor (TNF) regulates the two transcription factors, the nuclear factor κB (NFκB) and the activating transcription factor-2 (ATF-2). These two system outputs are involved in important physiological functions such as cell death and survival, viral replication, and pathological conditions, such as autoimmune diseases and different types of cancer. Using the introduced methods, we compute and show what the decision thresholds are, based on the single-cell measured concentration levels of NFκB and ATF-2. We also define and compute the decision error probabilities, i.e., false alarm and miss probabilities, based on the concentration levels of the two outputs. By considering the joint response of the two outputs of the signaling system, one can learn more about complex cellular decision-making processes, the corresponding decision error rates, and their possible involvement in the development of some pathological conditions.

Association Between Risk for Prediabetes and Type 2 Diabetes Mellitus Prevention Among Faculty Members and Administrative Staff of a Saudi University.

BACKGROUND: Diabetes mellitus prevalence continues to rise globally, causing disability and decreased productivity among patients, a significant strain on healthcare systems, and a burden on national economies. In 2021, diabetes will affect approximately 537 million adults. The rising prevalence of prediabetes worldwide also poses a significant public health threat, as it is estimated that by 2030, more than 470 million individuals will be prediabetic. OBJECTIVE: This study aimed to determine the association between the risk of prediabetes and the level of Type 2 Diabetes Mellitus (T2DM) prevention among faculty members and administrative staff of a Saudi university. METHODS: An analytic cross-sectional study design was utilized. The prediabetes risk of respondents was assessed using a risk test developed by the CDC, while the participants' diabetes prevention practices were determined using a researcher-developed questionnaire. Data were collected from 360 selected faculty members and administrative staff of three randomly selected health colleges and three non-health colleges at King Faisal University, Hofuf, Al-Ahsa, Saudi Arabia, between September 25 and October 13, 2022. The collected data were subjected to estimation of proportion and logistic regression analyses using Epi InfoTM version 7. RESULTS: Nearly 40% of respondents (39.72%, 95% CI: 34.80, 44.86) were found to be at high risk for prediabetes. The majority of university faculty and administrative staff consistently practiced T2DM preventive measures related to the limitation of processed food consumption, smoking cessation, and regular checking of weight and the nutritional value of food. However, there was poor T2DM prevention practice in terms of exercise, consumption of sweetened beverages, and stress reduction. Those who had a high prediabetes risk were 1.17 times more likely to engage in T2DM prevention practices. However, they were found to be 19% less likely to perform T2DM prevention practices when sociodemographic variables were held constant. CONCLUSION: Prediabetes risk was prevalent among Saudi university faculty and administrative staff. T2DM prevention was not consistently practiced by those who had a high risk for prediabetes. High prediabetes risk was negatively associated with the level of T2DM prevention.

Underwater Acoustic Signal Acquisition and Sensing Using a Ring Vector Sensor Communication Receiver: Theory and Experiments.

Signal acquisition and sensing in underwater systems and applications is typically a challenging issue due to the small signal strength within the background noise. Here, we present a ring vector sensor communication receiver that can significantly improve signal acquisition, by utilizing the underwater acoustic vector field components, compared to the scalar component. The vector sensor receiver is a multichannel receiver that measures particle velocities, which are vector components of the underwater acoustic field, in addition to the scalar field component. According to the combination of our measured experimental data with our signal acquisition performance analysis, the introduced ring vector sensor receiver exhibits higher signal acquisition probabilities for the vector components compared to the scalar component. This can be attributed to certain characteristics of the vector field components. Another advantage of this multichannel receiver is that combining all of its channels can further increase the signal acquisition and packet detection probability in underwater communication systems compared to a single-channel approach.

Intra-articular clodronate in patients with knee osteoarthritis non-responder to intra-articular hyaluronic acid - a case report series of 9 patients with 8-month follow-up.

Background: Osteoarthritis (OA) is a common disease in the elderly people, inducing pain and functional limitations. Clodronate (CLO) a first generation non-nitrogen containing bisphosphonate has been purposed as a treatment of OA, being effective on pain, inflammation, bone marrow oedema, osteophytosis and cartilage regeneration. Intra-muscular routes of CLO showed efficacy in the treatment of Knee OA (KOA) and erosive OA of the hand. In KOA intraarticular CLO at low doses (0.5-2 mg) showed efficacy as well as hyaluronic acid (HA), being able to improve the effectiveness if associated to HA. Methods: Nine Consecutive patients (4 female, 5 male, mean age 78,22) with KOA at 2nd or 3rd degree following Kellgren-Lawrance scale, non responder to HA and unintended to surgery. They were treated with intraarticular CLO at the weekly dose of 20 mg, plus lidocaine 1% in 5 cc of saline solution for a route of 5 weekly infil-trations, followed by a second route of 5 intraarticular infiltrations 3 months after the first course. Visual analog score (VAS) pain and Tegner-Lysholm Score (TLS) were used to assess changes following CLO treatment. Results: Baseline pain was 6,77/10, reduced to 1,09 at day 150 (after second course) and to 2,3/10 at day 240. TLS at baseline was 56,7/100, improved to 96,7 at day 150 and to 84,1 at day 240. At day 240 only 2 out of 9 patients had a negative judgement of the treatment and decided to stop it, while 7 were satisfied and available to a further course. There was no increase of consumption of anti-inflammatory or analgesic drugs. A short time lasting pain after the injections was registered in all patients. Conclusions: In a small cohort of patients affected by KOA, non responders to intraarticular HA a higher dose of intraarticular CLO in KOA showed good compliance, amelioration of pain and functionality.

Learning feedback molecular network models using integer linear programming.

Analysis of intracellular molecular networks has many applications in understanding of the molecular bases of some complex diseases and finding effective therapeutic targets for drug development. To perform such analyses, the molecular networks need to be converted into computational models. In general, network models constructed using literature and pathway databases may not accurately predict experimental network data. This can be due to the incompleteness of literature on molecular pathways, the resources used to construct the networks, or some conflicting information in the resources. In this paper, we propose a network learning approach via an integer linear programming formulation that can systematically incorporate biological dynamics and regulatory mechanisms of molecular networks in the learning process. Moreover, we present a method to properly consider the feedback paths, while learning the network from data. Examples are also provided to show how one can apply the proposed learning approach to a network of interest. In particular, we apply the framework to the ERBB signaling network, to learn it from some experimental data. Overall, the proposed methods are useful for reducing the gap between the curated networks and experimental data, and result in calibrated networks that are more reliable for making biologically meaningful predictions.

Cytokine patterns in cystic fibrosis patients with different microbial infections in oropharyngeal samples.

BACKGROUND: Cytokines play a crucial role in the immune system's regulation by mediating protective responses to infections. anti-inflammatory and pro-inflammatory cytokines are in equilibrium. Therefore, any alteration in cytokine production or cytokine receptor expression might result in pathological illnesses and health issues. Cystic fibrosis (CF) is a genetic disease caused by mutations in the CF transmembrane regulator (CFTR) gene. Lung infection in these patients is related to chronic bacterial airway infection and inflammation, which is triggered by some inflammatory cytokines. Our goal was to compare the cytokine patterns in CF patient's serum and PBMCs caused by microbial pathogens that colonized their airways to controls. METHODS: ELISA and Real-time PCR were used to determine the levels of IL-10, IFN-γ, IL-4, TGF-ß, IL-8, and IL-17 in serum and PBMC cells. Blood parameters in both patients and healthy people were studied. RESULTS: An increase in IL-10, IFN-γ, IL-4 (p-v = 0.03, 0.024 and 0.003) levels and a decrease in IL-17 (p-v = 0.004) was found in Pseudomonas aeruginosa positive patients. There were no different in TGF-ß and IL-8 (p-value = 0.778 and 0.903) in this patients. IL-10, IFN-γ, and IL-4 (p-value = 0.023, 0.001 and 0.002) levels were high in Staphylococcus aureus positive patients and TGF-ß, IL-17, and IL-8 (p-value = 0.085, 0.167 and 0.362) were not significantly different in the patient and control groups. IFN-γ and IL-4 levels were higher in patients without infection who had normal microbiota (p-v = 0.002 and 0.024). In patients with P. aeruginosa, WBC and platelets increased, and MCH and MCV decreased. Patients with normal microbiota had less MCV. CONCLUSION: According to our research, patients with P. aeruginosa, S. aureus, and normal microbiota are exposed to cytokine alterations and changes in blood factors. The link between the CF patient's airway microbiota and the kind of generated cytokines might lead to the modulation of inflammatory cytokines alone or in combination with antibiotics, reducing disease-causing effects while avoiding drug resistance.

A hybrid model to study how late long-term potentiation is affected by faulty molecules in an intraneuronal signaling network regulating transcription factor CREB.

Systems biology analysis of intracellular signaling networks has tremendously expanded our understanding of normal and diseased cell behaviors and has revealed paths to finding proper therapeutic molecular targets. When it comes to neurons in the human brain, analysis of intraneuronal signaling networks provides invaluable information on learning, memory and cognition-related disorders, as well as potential therapeutic targets. However, neurons in the human brain form a highly complex neural network that, among its many roles, is also responsible for learning, memory formation and cognition. Given the impairment of these processes in mental and psychiatric disorders, one can envision that analyzing interneuronal processes, together with analyzing intraneuronal signaling networks, can result in a better understanding of the pathology and, subsequently, more effective target discovery. In this paper, a hybrid model is introduced, composed of the long-term potentiation (LTP) interneuronal process and an intraneuronal signaling network regulating CREB. LTP refers to an increased synaptic strength over a long period of time among neurons, typically induced upon occurring an activity that generates high-frequency stimulations (HFS) in the brain, and CREB is a transcription factor known to be highly involved in important functions of the cognitive and executive human brain such as learning and memory. The hybrid LTP-signaling model is analyzed using a proposed molecular fault diagnosis method. It allows to study the importance of various signaling molecules according to how much they affect an intercellular phenomenon when they are faulty, i.e. dysfunctional. This paper is intended to suggest another angle for understanding the pathology and therapeutic target discovery by classifying and ranking various intraneuronal signaling molecules based on how much their faulty behaviors affect an interneuronal process. Possible relations between the introduced hybrid analysis and the previous purely intracellular analysis are investigated in the paper as well.

Exploring extreme signaling failures in intracellular molecular networks.

Developing novel methods for the analysis of intracellular signaling networks is essential for understanding interconnected biological processes that underlie complex human disorders. A fundamental goal of this research is to quantify the vulnerability of a signaling network to the dysfunction of one or multiple molecules, when the dysfunction is defined as an incorrect response to the input signals. In this study, we propose an efficient algorithm to identify the extreme signaling failures that can induce the most detrimental impact on the physiological function of a molecular network. The algorithm finds the molecules, or groups of molecules, with the maximum vulnerability, i.e., the highest probability of causing the network failure, when they are dysfunctional. We propose another algorithm that efficiently accounts for signaling feedbacks. The algorithms are tested on experimentally verified ERBB and T-cell signaling networks. Surprisingly, results reveal that as the number of concurrently dysfunctional molecules increases, the maximum vulnerability values quickly reach to a plateau following an initial increase. This suggests the specificity of vulnerable molecule(s) involved, as a specific number of faulty molecules cause the most detrimental damage to the function of the network. Increasing the number of simultaneously faulty molecules does not further deteriorate the network function. Such a group of specific molecules whose dysfunction causes the extreme signaling failures can better elucidate the molecular mechanisms underlying the pathogenesis of complex trait disorders, and can offer new insights for the development of novel therapeutics.

Computer Vision-Based Path Planning for Robot Arms in Three-Dimensional Workspaces Using Q-Learning and Neural Networks.

Computer vision-based path planning can play a crucial role in numerous technologically driven smart applications. Although various path planning methods have been proposed, limitations, such as unreliable three-dimensional (3D) localization of objects in a workspace, time-consuming computational processes, and limited two-dimensional workspaces, remain. Studies to address these problems have achieved some success, but many of these problems persist. Therefore, in this study, which is an extension of our previous paper, a novel path planning approach that combined computer vision, Q-learning, and neural networks was developed to overcome these limitations. The proposed computer vision-neural network algorithm was fed by two images from two views to obtain accurate spatial coordinates of objects in real time. Next, Q-learning was used to determine a sequence of simple actions: up, down, left, right, backward, and forward, from the start point to the target point in a 3D workspace. Finally, a trained neural network was used to determine a sequence of joint angles according to the identified actions. Simulation and experimental test results revealed that the proposed combination of 3D object detection, an agent-environment interaction in the Q-learning phase, and simple joint angle computation by trained neural networks considerably alleviated the limitations of previous studies.

One step green synthesis of Cu nanoparticles by the aqueous extract of Juglans regia green husk: assessing its physicochemical, environmental and biological activities.

Juglans regia (J. regia) green husk is an abundant agricultural waste. In this study, an economical, rapid and green synthetic route was introduced for the biosynthesis of copper nanoparticles (CuNPs) by applying the aqueous extract of J. regia green husk at the ambient conditions. Ultra Violet-Visible (UV-Visible) analysis revealed that the Surface Plasmon Resonance (SPR) of the CuNP was 212 nm. The average hydrodynamic and metallic core diameters of the CuNPs were about 53-28 nm, respectively. X-ray Diffraction (XRD) analysis presented that the CuNPs were amorphous. The CuNPs exhibited the highest free radical 1,1-diphenyl-2-picryl-hydrazyl (DPPH) scavenging efficiency. These nanoparticles (NPs) showed antibacterial, antifungal and antibiofilm properties. They presented photocatalytic activity against Methyl Orange (MO). Besides, the potential of these NPs for the fast and precise colorimetric detection of Hg2+ was remarkable. The biosynthesized CuNPs are introduced as a multifunctional nanomaterial with various applications in medicine and environmental cases. The CuNPs were produced through an environmentally green process by the aqueous extract of dried J. regia green husk at the ambient condition. The CuNPs confirmed that this type of nanomaterial is a multifunctional agent with significant antibacterial, antifungal, antibiofilm, antioxidant, photocatalytic activities. Besides, it is a promising colorimetric sensor for the detection of Hg2+ in an aqueous complex media.

New white light-emitting halochromic stilbenes with remarkable quantum yields and aggregation-induced emission.

Highly efficient single-component white light emitters (SWLEs), are attractive candidates for the simple and cost-effective fabrication of high-performance lighting devices. This study introduced a donor-π-acceptor and a donor-π-donor stilbene-based chromophores, representing pH-responsive fluorescence. The emitters showed yellow and green fluorescence in their neutral form. At the same time, protonation of the chromophores caused blue fluorescence color with a strong hypsochromic shift. The white light emission (WLE) for these chromophores was observed at approximately pH 3 due to the simultaneous presence of the neutral and protonated forms of the chromophores, covering almost all the emission spectra in the visible region (400-700 nm). These chromophores presented exceptional white light quantum yields (Φ) between 31 and 54%, which was desirable for producing white light-emitting devices. Density functional theory (DFT) and time-dependent (TD)-DFT were applied to study the structural and electronic properties of the chromophores.

Involvement of Pseudomonas aeruginosa in the occurrence of community and hospital acquired diarrhea, and its virulence diversity among the stool and the environmental samples.

Transmission of Pseudomonas aeruginosa along the food chain could cause gastrointestinal infections. To show this involvement, the prevalence, putative virulence genotype, and antibiotic resistance phenotype of P. aeruginosa isolates from stool of 1482 patients with community and hospital acquired diarrhea were compared with 87 isolates from the environmental samples. The results showed infection with P. aeruginosa in 3.4% of the cases, while 57.4% of vegetable samples were contaminated. Significantly higher frequency of lasB (98%), aprA (98%), exoY (98%), and exoS (90%), but lower rate of exoT (39.2%), was detected among the stool isolates. Multi-drug resistance (MDR) phenotype was detected in 25.5% and 4% of the stool and vegetable isolates, respectively. A higher rate of studied virulence genes was detected among the MDR strains vs non-MDR strains. These results indicate P. aeruginosa as a causative agent of diarrhea either among the hospitalized patients and those with community-acquired diarrhea.

Bacterial variation in the oral microbiota in multiple sclerosis patients.

BACKGROUND: Microorganisms in oral cavity are called oral microbiota, while microbiome consists of total genome content of microorganisms in a host. Interaction between host and microorganisms is important in nervous system development and nervous diseases such as Autism, Alzheimer, Parkinson and Multiple Sclerosis (MS). Bacterial infections, as an environmental factor in MS pathogenesis play role in T helper 17(Th17) increase and it enhancing the production of pro-inflammatory cytokines such as Interlukin-21(IL-21), IL-17 and IL -22. Oral microbiota consists diverse populations of cultivable and uncultivable bacterial species. Denaturing gradient gel electrophoresis (DGGE) is an acceptable method for identification of uncultivable bacteria. In this study, we compared the bacterial population diversity in the oral cavity between MS and healthy people. METHODS: From October to March 2019, samples were taken at Kermanshah University of Medical Sciences' MS patients center. A total of 30 samples were taken from MS patients and another 30 samples were taken from healthy people. Phenotypic tests were used to identify bacteria after pure cultures were obtained. DNA was extracted from 1 mL of saliva, and PCR products produced with primers were electrophoresed on polyacrylamide gels. RESULTS: The genera Staphylococcus, Actinomyces, Fusobacterium, Bacteroides, Porphyromonas, Prevotella, Veillonella, Propionibacterium and uncultivable bacteria with accession number MW880919-25, JQ477416.1, KF074888.1 and several other un-culturable strains were significantly more abundant in the MS group while Lactobacillus and Peptostreptococcus were more prevalent in the normal healthy group according to logistic regression method. CONCLUSION: Oral micro-organisms may alleviate or exacerbate inflammatory condition which impact MS disease pathogenesis. It may be assumed that controlling oral infections may result in reduction of MS disease progression.

Immunoinformatics design of multivalent chimeric vaccine for modulation of the immune system in Pseudomonas aeruginosa infection.

Increasing in drug-resistant Pseudomonas aeruginosa and high mortality and morbidity rate have become a health challenge worldwide; therefore, developing the novel therapeutic strategies such as immunogenic vaccine candidate are required. Despite a substantial research effort, the future of immunization against P. aeruginosa due to failure in covering two separate stages of infection, and furthermore, inducing ineffective type of immune response, still remains controversial. In this study, immunoinformatics approach was utilized to design multivalent chimeric vaccine from both stages of infection containing Lectin, HIV TAT peptide, N-terminal fragment of exotoxin A and Epi8 of outer membrane protein F (OprF) with hydrophobic linkers which have a high density of B-cell, T Lymphocytes (HTL), T Lymphocytes (CTL), and IFN-γ epitopes. The physicochemical properties, antigenicity, and allergenicity for designed vaccine were analyzed. 3D model generation and refinement further validation of the final vaccine were followed by computational docking with molecular dynamics analyses that demonstrated high- affinity interaction between vaccine and TLR-4. Finally, designed vaccine was in silico cloned in pET22b. We have expected that the designed vaccine able to elucidate innate, humoral and cellular innate immune responses and control the interaction of P. aeruginosa with host and maybe overcome to P. aeruginosa vaccines drawback.

Use of clodronate for painful knee prosthesis in osteoarthritis patients: a 6-month pilot study.

BACKGROUND: Knee replacement surgery is one of the most common surgical procedures performed worldwide. Unfortunately, knee prostheses can become painful over time, necessitating appropriate analgesic treatment. Bisphosphonates such as clodronate (CLO) may play an important role in the treatment of painful knee prostheses by virtue of its analgesic and anti-inflammatory properties. METHODS: In this prospective open label pilot study, eighteen consecutive patients aged 73.2±8.9 years affected by knee painful prosthesis and osteoarthritis were treated with a rehabilitation cycle in addition to i.v. or i.m. CLO. Induction dose was 2.0-2.1g, followed by a weekly dose of 200 mg (i.m.) for 6 months. Visual analogue scale (VAS) pain score and Tegner Lysholm Score (TLS) were used to assess improvement following CLO treatment. RESULTS: Thirteen out of 18 patients completed the 6-month follow-up. VAS pain score decreased from 8.1±1.8 at baseline to 5.6±2.6 (P<0.05) and TLS increased from 40.4±20.3 at baseline to 62.7±24.1 at 6 months (P<0.05). Univariate regression revealed that among a range of variables, BMI was positively correlated with VAS (r=0.73, P=0.004) and lower TLS after 1 month (r= -0.62, P=0.006). CONCLUSIONS: CLO in association with rehabilitation exercises can reduce pain and ameliorate the functionality of painful knee prostheses. Administration of a high dose (induction dose) of CLO every 3 months appears to be the most effective regimen compared to a weekly maintenance dose.

Biochemical and computational study of an alginate lyase produced by Pseudomonas aeruginosa strain S21.

OBJECTIVES: Alginates play a key role in mucoid Pseudomonas aeruginosa colonization, biofilm formation, and driving out of cationic antibiotics. P. aeruginosa alginate lyase (AlgL) is a periplasmic enzyme that is necessary for alginate synthesis and secretion. It also has a role in depolymerization of alginates. Using AlgLs in cystic fibrosis patients along with antibiotics enhances bacterial killing and host healing. In this study, we investigated the different biochemical properties of a newly isolated AlgL from P. aeruginosa S21 to complete the databank of AlgLs. MATERIALS AND METHODS: The enzyme was extracted from the periplasmic space of the bacteria by the heat shock method. Using the TBA method, the enzyme activity and biochemical properties were assessed. The mutability of P. aeruginosa S21 AlgL to increase its thermal stability was investigated. The most favorable mutations were studied computationally. The molecular dynamics simulation (MDS) package GROMACS was used for determining the effect of S34R mutation on enzyme's thermal stability. RESULTS: Data showed that this enzyme has the best activity at 37 °C and pH 7.5 and it can degrade mannuronate blocks, guluronate blocks, and sodium alginate. After 7 hr at 80 °C, 45% of the enzyme activity was retained. This enzyme needed 15 min to completely degrade accessible sodium alginate. Tris buffer, pH 8.5 and Britton-Robinson buffer, pH 7.0 were the preferable buffers for the enzyme activity. MDS of native and mutated enzymes showed desirable results. CONCLUSION: P. aeruginosa S21 AlgL can be used in medical and industrial applications to degrade alginates.

Short-term Forecasting of Cumulative Confirmed Cases of Covid-19 Pandemic in Somalia

Somalia has recorded the first confirmed Covid-19 case and first death case on March 16, and April 08, 2020, respectively. Since its arrival, it had infected 2,603 people and took the lives of 88 people while 577 patients were recovered as of 14 June, 2020. To fight this pandemic, the government requires to make the necessary plans accordingly. To plan effectively, the government needs to answer this question: what will be the effect of Covid-19 cases in the country? To answer this question accurately and objectively, forecasting the spread of confirmed Covid-19 cases will be vital. To this regard, this paper provides real times forecasts of Covid-19 cases employing Holts linear trend model without seasonality. Provided that the data employed is accurate and the past pattern of the disease will continue in the future, this model is powerful to produce real time forecasts in the future with some degree of uncertainty. With the help of these forecasts, the government can make evidence based decisions by utilizing the scarce resource available at its disposal.

Modeling and measurement of signaling outcomes affecting decision making in noisy intracellular networks using machine learning methods.

Characterization of decision-making in cells in response to received signals is of importance for understanding how cell fate is determined. The problem becomes multi-faceted and complex when we consider cellular heterogeneity and dynamics of biochemical processes. In this paper, we present a unified set of decision-theoretic, machine learning and statistical signal processing methods and metrics to model the precision of signaling decisions, in the presence of uncertainty, using single cell data. First, we introduce erroneous decisions that may result from signaling processes and identify false alarms and miss events associated with such decisions. Then, we present an optimal decision strategy which minimizes the total decision error probability. Additionally, we demonstrate how graphing receiver operating characteristic curves conveniently reveals the trade-off between false alarm and miss probabilities associated with different cell responses. Furthermore, we extend the introduced framework to incorporate the dynamics of biochemical processes and reactions in a cell, using multi-time point measurements and multi-dimensional outcome analysis and decision-making algorithms. The introduced multivariate signaling outcome modeling framework can be used to analyze several molecular species measured at the same or different time instants. We also show how the developed binary outcome analysis and decision-making approach can be extended to more than two possible outcomes. As an example and to show how the introduced methods can be used in practice, we apply them to single cell data of PTEN, an important intracellular regulatory molecule in a p53 system, in wild-type and abnormal cells. The unified signaling outcome modeling framework presented here can be applied to various organisms ranging from viruses, bacteria, yeast and lower metazoans to more complex organisms such as mammalian cells. Ultimately, this signaling outcome modeling approach can be utilized to better understand the transition from physiological to pathological conditions such as inflammation, various cancers and autoimmune diseases.