Statistical optimization of bioprocess parameters for enhanced production of bacterial cellulose from K. saccharivorans BC-G1.

Bacterial Cellulose (BC) offers a wide range of applications across various industries, including food, biomedical, and textiles, owing to its distinctive properties. Its unique 3D reticulated network of cellulose nanofibers, imparts excellent mechanical qualities, a high water-holding capacity, and thermal stability. Additionally, it possesses remarkable biocompatibility, biodegradability, high crystallinity, and purity. These attributes have offered significant interest in BC within both academic and industrial sectors. However, BC production is associated with high costs due to the use of expensive growth media and low yields. The study reports the potential of our indigenous isolate, Komagataeibacter saccharivorans BC-G1, as BC producer. Statistical optimization of BC production was carried out using Placket-Burman design and Central composite design, by selecting different parameters. Eight significant factors such as temperature, pH, glucose, yeast, peptone, acetic acid, incubation time and % inoculum were studies using ANOVA-based response surface methodology. Results showed that BC yield (8.5 g/L) with 1.8-fold after optimization of parameters. Maximum cellulose production (8.5 ± 1.8 g/L) was obtained using 2% glucose, 0.3% yeast extract, 0.3% peptone, 0.75% (v/v) acetic acid at pH 7.0 for 10 days of incubation with 4% inoculum at 25 °C under static culture. Main effect graph showed incubation time and acetic acid concentration as the most significant parameters affecting BC production in our study. The physicochemical characterization of produced BC was done using FTIR, XRD and SEM techniques.

A review on blockchain for DNA sequence: security issues, application in DNA classification, challenges and future trends.

In biological science, the study of DNA sequences is considered an important factor because it carries the genomic details that can be used by researchers and doctors for the early prediction of disease using DNA classification. The NCBI has the world's largest database of genetic sequences, but the security of this massive amount of data is currently the greatest issue. One of the options is to encrypt these genetic sequences using blockchain technology. As a result, this paper presents a survey on healthcare data breaches, the necessity for blockchain in healthcare, and the number of research studies done in this area. In addition, the report suggests DNA sequence classification for earlier disease identification and evaluates previous work in the field.

Synthesis, Characterization, and Antibacterial Activity of New Isatin Derivatives.

1H-indol-2,3-dione (isatin) class of biologically active compounds have analgesic, anti-microbial, anti-inflammatory, anti-tubercular, anti-proliferative properties, and is also useful for the treatment of SARS-CoV. Schiff bases containing isatin moiety are known to have broad spectrum of biological activities like anti-viral, anti-tubercular, anti-fungal, and anti-bacterial. In this work, several Schiff base derivatives have been synthesized using two methods (synthetic and microwave) by reacting isatin with o-phenylenediamine. The synthesized compounds were structurally characterized and their in-vivo antimicrobial activity was tested against Gram-negative and Gram-positive bacteria using the inhibition zone method. Several newly synthesized isatin derivatives were found effective as antimicrobial agents and showed good potency (compounds 3c, 3d, 6a, 6b, 6d). Compound 3c displayed higher antimicrobial activity than standard drug (Amoxicillin) against Staphylococcus aureus at higher concentration (16 µg/mL) and against Escherichia coli at lower concentration (1 µg/mL).

A comprehensive tool for rapid and accurate prediction of disease using DNA sequence classifier.

In the current pandemic situation where the coronavirus is spreading very fast that can jump from one human to another. Along with this, there are millions of viruses for example Ebola, SARS, etc. that can spread as fast as the coronavirus due to the mobilization and globalization of the population and are equally deadly. Earlier identification of these viruses can prevent the outbreaks that we are facing currently as well as can help in the earlier designing of drugs. Identification of disease at a prior stage can be achieved through DNA sequence classification as DNA carries most of the genetic information about organisms. This is the reason why the classification of DNA sequences plays an important role in computational biology. This paper has presented a solution in which samples collected from NCBI are used for the classification of DNA sequences. DNA sequence classification will in turn gives the pattern of various diseases; these patterns are then compared with the samples of a newly infected person and can help in the earlier identification of disease. However, feature extraction always remains a big issue. In this paper, a machine learning-based classifier and a new technique for extracting features from DNA sequences based on a hot vector matrix have been proposed. In the hot vector representation of the DNA sequence, each pair of the word is represented using a binary matrix which represents the position of each nucleotide in the DNA sequence. The resultant matrix is then given as an input to the traditional CNN for feature extraction. The results of the proposed method have been compared with 5 well-known classifiers namely Convolution neural network (CNN), Support Vector Machines (SVM), K-Nearest Neighbor (KNN) algorithm, Decision Trees, Recurrent Neural Networks (RNN) on several parameters including precision rate and accuracy and the result shows that the proposed method gives an accuracy of 93.9%, which is highest compared to other classifiers.

Evaluation of physicochemical and biological properties of chitosan/poly (vinyl alcohol) polymer blend membranes and their correlation for Vero cell growth.

The blend membranes with varying weight ratios of chitosan/poly (vinyl alcohol) (CS/PVA) (1:0, 1:1, 1:2.5, 1.5:1, 1.5: 2.5) were prepared using solvent casting method and were evaluated for their potential application in single-use membrane bioreactors (MBRs). The physicochemical properties of the prepared membranes were investigated for chemical interactions (FTIR), surface morphology (SEM), water uptake, protein sorption (qe), ammonia sorption and growth kinetics of Vero cells. CS/PVA blend membrane having weight ratio of 1.5:1 had shown enhanced membrane flexibility, reduced water uptake, less protein sorption and no ammonium sorption compared to CS membrane. This blend membrane also showed comparatively enhanced higher specific growth rate (0.82/day) of Vero cells. Improved physicochemical properties and growth kinetics obtrude CS/PVA (1.5:1) as a potential surface for adhesion and proliferation with possible application in single use membrane bioreactors. Additionally, new insight explaining correlation between water holding (%) of CS/PVA (1.5:1) blend membrane and doubling time (td) of Vero cells is proposed.

Effect of media components on cell growth and bacterial cellulose production from Acetobacter aceti MTCC 2623.

Acetobacter aceti MTCC 2623 was studied as an alternative microbial source for bacterial cellulose (BC) production. Effect of media components on cell growth rate, BC production and cellulose characteristics were studied. FTIR results showed significant variations in cellulose characteristics produced by A. aceti in different media. Results have shown the role of fermentation time on crystallinity ratio of BC in different media. Further, effect of six different media components on cell growth and BC production was studied using fractional factorial design. Citric acid was found to be the most significant media component for cell growth rate (95% confidence level, R(2)=0.95). However, direct role of these parameters on cellulose production was not established (p-value>0.05).

Degradation of polyurethane by Aspergillus flavus (ITCC 6051) isolated from soil.

The present study deals with the isolation of fungi from soil with the ability to degrade polyurethane (PU). A pure fungal isolate was analyzed for its ability to utilize PU as a sole carbon source in shaking culture for 30 days. Incubation of PU with Aspergillus flavus resulted in 60.6% reduction in weight of PU. The scanning electron microscopy and Fourier transform infrared spectroscopy (FTIR) results showed certain changes on the surface of PU film and formation of some new intermediate products after polymer breakdown. Thermogravimetric curves showed changes between the thermal behavior of the samples that were inoculated with A. flavus and control. FTIR spectra showed detectable changes in control and incubated samples, suggesting that degradation occurs, with the decreased intensity of band at 1,715 cm(-1), corresponding to ester linkages. We have identified an extracellular esterase activity which might be responsible for the polyurethanolytic activity.

Epidemiological review of gastric cancer in India.

Stomach cancer is the one of the leading cause of cancer in southern region of India. Its incidence is decreasing worldwide yet on global scale stomach cancer remains one of the most common causes of cancer death. Etiology of gastric cancer includes Helicobacter pylori infection, diet and lifestyle, tobacco, alcohol and genetic susceptibility. In this review, we tried to find the contribution of Indian scientist in understanding the descriptive and observational epidemiology of stomach cancer. PubMed was used as a search platform using key words such as "stomach cancer, treatment, clinical characteristics, stomach cancer outcome, epidemiology, etiological factor and their corresponding Mesh terms were used in combination with Boolean operators OR, AND". Most of the reported studies on gastric cancer from India are case report or case series and few are case-control studies. Indian studies on this topic are limited and have observed H. pylori infection, salted tea, pickled food, rice intake, spicy food, soda (additive of food), tobacco and alcohol as risk factors for gastric cancer. More research is required to understand the etiology, develop suitable screening test, to demarcate high-risk population and to develop and evaluate the effect of primary prevention programs.

Risk Factor Scenario in an Industrial Set-up: Need for an Effective Screening Tool to Assess the High-Risk Group.

BACKGROUND: Industrial and technological revolution has resulted in nutrition transition. This calls for analyzing the risk factor scenario in the industrial population. OBJECTIVE: The objective was to map the prevalence and assess the risk factors of industrial employees. MATERIALS AND METHODS: The employees of a large petrochemical industry were enrolled (N=269) for the study. Risk factors were elicited through a structured questionnaire. Parameters monitored were fasting blood sugar and lipid profile. Relative risk was calculated to find out significant predictor variables. RESULTS: The employees had high prevalence of overweight (27%), obesity (22%), central obesity (48.7%), prehypertension (43.2%), hypertension (36.6%), and dyslipidemia (41.4%). They had erroneous dietary habits such as low intake of fruits and vegetables and high fat intake. Most of the employees had low physical activity levels. The prevalence of smoking (13.5%), tobacco (28.2%), and alcohol use (22.2%) were also high with 15.1% having multiple habits. One-fifth of the employees had metabolic syndrome (MS). Seven predictor variables, namely, family history, BMI, WHR, blood pressure, physical inactivity, TG, and TG/H were identified and used to develop the risk score card to identify people at high risk of CVD and DM. CONCLUSION: Multiple risk factor scenario among the industrial population studied calls for effective intervention strategies and policy changes to combat the burden of non-communicable diseases. The risk score card can be used to screen the high-risk group in the industrial population.

Toward a fuzzy logic control of the infant incubator.

Premature birth is a world wide problem. Thermo regulation is a major problem in premature infants. Premature infants are often kept in infant incubators providing convective heating. Currently either the incubator air temperature is sensed and used to control the heat flow, or infant's skin temperature is sensed and used in the close loop control. Skin control often leads to large fluctuations in the incubator air temperature. Air control also leads to skin temperature fluctuations. The question remains if both the infant's skin temperature and the incubator air temperature can be simultaneously used in the control. The purpose of the present study was to address this question by developing a fuzzy logic control which incorporates both incubator air temperature and infant's skin temperature to control the heating. The control was evaluated using a lumped parameter mathematical model of infant-incubator system (Simon, B. N., N. P. Reddy, and A. Kantak, J. Biomech. Eng. 116:263-266, 1994). Simulation results confirmed previous experimental results that the on-off skin control could lead to fluctuations in the incubator air temperature, and the air control could lead to too slow rise time in the core temperature. The fuzzy logic provides a smooth control with the desired rise time.