EFFECT OF CULTURE PARAMETERS ON PROTEASE AND CELLULASE PRODUCTION BY TWO BACTERIAL STRAINS, Corynebacterium alkanolyticum ATH3 AND Bacillus licheniformis CBH7 ISOLATED FROM FISH GUT

Microbial protease and cellulase are in high demand by different industries due to their minimal cost and availability. This study was aimed to maximize the production of protease and cellulase using two bacteria, Corynebacterium alkanolyticum ATH3 and Bacillus licheniformis CBH7, isolated from fish gut. This study demonstrated the effect of different culture parameters in protease and cellulase production using two different bacterial strains. Results of this study clearly indicated the importance of different parameters such as moisture content, pH, incubation temperature, incubation period, inoculum size, carbon sources and nitrogen sources in enzyme production. The most critical parameters affecting the enzymes production were pH, temperature, carbon and nitrogen sources. Further investigations are required to enhance the enzymes production using genetic engineering

A COMPARISON OF POST-OPERATIVE ANALGESIA WITH INTRAOPERATIVE PECTORAL NERVE BLOCK VERSUS CONVENTIONAL TECHNIQUE IN PATIENTS UNDERGOING MODIFIED RADICAL MASTECTOMY: A PROSPECTIVE, RANDOMIZED, AND DOUBLE-BLINDED STUDY

Objective: We administered intraoperative pectoral nerve block after tissue resection was over and assessed its analgesic efficacy with conventional post-operative intravenous opioids in patients undergoing modified radical mastectomy. Methods: Sixty patients undergoing modified radical mastectomy surgery were enrolled in this prospective, randomized, and doubleblinded study. After general anesthesia and surgical resection in both groups, Group P received pectoralis (PECS) block under vision with ropivacaine at two points: 20 ml in the fascia over serratus anterior and 10 ml in the fascia between pectoral major and minor at the level of the third rib and Group T received tramadol (75 mg) in thrice daily frequency and 2% lignocaine infiltration at suture site. Primary objectives were to assess visual analog scale (VAS) scores over 24 h, time to first request for rescue analgesia (ketorolac) and total dose of analgesics needed, and secondary outcome was adverse effects and patient satisfaction score. “Mann–Whitney U test” and “Chi-square/Fischer exact test” were used for quantitative and categorical variables, respectively. Results: The mean time to the first rescue analgesia was 1175±120.21 and 1175±77.35 min and total analgesia requirement was equal (30.00±0.00 mg) in Group P and Group T, respectively. The mean VAS score over 24 h was comparable in both the groups. PECS block group had significantly less adverse effects and better satisfaction score. Conclusion: PECS block has similar analgesic efficacy as opioids but with better ability to mobilize the respective arm, better patient satisfaction score, and lesser adverse effects

Predicting Thermoelectric Power Factor of Bismuth Telluride During Laser Powder Bed Fusion Additive Manufacturing

An additive manufacturing (AM) process, like laser powder bed fusion, allows for the fabrication of objects by spreading and melting powder in layers until a freeform part shape is created. In order to improve the properties of the material involved in the AM process, it is important to predict the material characterization property as a function of the processing conditions. In thermoelectric materials, the power factor is a measure of how efficiently the material can convert heat to electricity. While earlier works have predicted the material characterization properties of different thermoelectric materials using various techniques, implementation of machine learning models to predict the power factor of bismuth telluride (Bi2Te3) during the AM process has not been explored. This is important as Bi2Te3 is a standard material for low temperature applications. Thus, we used data about manufacturing processing parameters involved and in-situ sensor monitoring data collected during AM of Bi2Te3, to train different machine learning models in order to predict its thermoelectric power factor. We implemented supervised machine learning techniques using 80% training and 20% test data and further used the permutation feature importance method to identify important processing parameters and in-situ sensor features which were best at predicting power factor of the material. Ensemble-based methods like random forest, AdaBoost classifier, and bagging classifier performed the best in predicting power factor with the highest accuracy of 90% achieved by the bagging classifier model. Additionally, we found the top 15 processing parameters and in-situ sensor features to characterize the material manufacturing property like power factor. These features could further be optimized to maximize power factor of the thermoelectric material and improve the quality of the products built using this material.Comment: 8 pages, 2 figures, 2 tables, accepted at Data Science for Smart Manufacturing and Healthcare workshop (DS2-MH) at SIAM International Conference on Data Mining (SDM23) conferenc

Leveraging Natural Learning Processing to Uncover Themes in Clinical Notes of Patients Admitted for Heart Failure

Heart failure occurs when the heart is not able to pump blood and oxygen to support other organs in the body as it should. Treatments include medications and sometimes hospitalization. Patients with heart failure can have both cardiovascular as well as non-cardiovascular comorbidities. Clinical notes of patients with heart failure can be analyzed to gain insight into the topics discussed in these notes and the major comorbidities in these patients. In this regard, we apply machine learning techniques, such as topic modeling, to identify the major themes found in the clinical notes specific to the procedures performed on 1,200 patients admitted for heart failure at the University of Illinois Hospital and Health Sciences System (UI Health). Topic modeling revealed five hidden themes in these clinical notes, including one related to heart disease comorbidities

Mining Themes in Clinical Notes to Identify Phenotypes and to Predict Length of Stay in Patients admitted with Heart Failure

Heart failure is a syndrome which occurs when the heart is not able to pump blood and oxygen to support other organs in the body. Identifying the underlying themes in the diagnostic codes and procedure reports of patients admitted for heart failure could reveal the clinical phenotypes associated with heart failure and to group patients based on their similar characteristics which could also help in predicting patient outcomes like length of stay. These clinical phenotypes usually have a probabilistic latent structure and hence, as there has been no previous work on identifying phenotypes in clinical notes of heart failure patients using a probabilistic framework and to predict length of stay of these patients using data-driven artificial intelligence-based methods, we apply natural language processing technique, topic modeling, to identify the themes present in diagnostic codes and in procedure reports of 1,200 patients admitted for heart failure at the University of Illinois Hospital and Health Sciences System (UI Health). Topic modeling identified twelve themes each in diagnostic codes and procedure reports which revealed information about different phenotypes related to various perspectives about heart failure, to study patients\u27 profiles and to discover new relationships among medical concepts. Each theme had a set of keywords and each clinical note was labeled with two themes - one corresponding to its diagnostic code and the other corresponding to its procedure reports along with their percentage contribution. We used these themes and their percentage contribution to predict length of stay. We found that the themes discovered in diagnostic codes and procedure reports using topic modeling together were able to predict length of stay of the patients with an accuracy of 61.1% and an Area under the Receiver Operating Characteristic Curve (ROC AUC) value of 0.828

Effect of magnetic field on jet transport coefficient q^\hat{q}

We report the estimation of jet transport coefficient, $\hat{q}$ for quark- and gluon-initiated jets using a simple quasi-particle model in absence and presence of magnetic field. This model introduces a temperature and magnetic field-dependent degeneracy factor of partons, which is tuned by fitting the entropy density of lattice quantum chromodynamics data. At a finite magnetic field, $\hat{q}$ for quark jets splits into parallel and perpendicular components whose magnetic field dependence comes from two sources: the field-dependent degeneracy factor and the phase space part guided from the shear viscosity to entropy density ratio. Due to the electrically neutral nature of gluons, the estimation of $\hat{q}$ for gluon jets is affected only by the field-dependent degeneracy factor. In presence of a finite magnetic field, we find a significant enhancement in $\hat{q}$ for both quark- and gluon-initiated jets at low temperature, which gradually decreases towards high temperature. We compare the obtained results with the earlier calculations based on the anti-de Sitter/conformal field theory correspondence, and a qualitatively similar trend is observed. The change in $\hat{q}$ in presence of magnetic field is, however, quantitatively different for quark- and gluon-initiated jets. This is an interesting observation which can be explored experimentally to verify the effect of magnetic field on $\hat{q}$.Comment: typos corrected, references added, results update

Nutritional evaluation of soybean meal after fermentation with two fish gut bacterial strains, Bacillus cereus LRF5 and Staphylococcus caprae CCF2 in formulated diets for Labeo rohita fingerlings

Twelve isonitrogenous (35 % crude protein) and isocaloric (18.0 kJ/g) diets were formulated incorporating raw and fermented soybean meal (SBM) at 15%, 30%, 45% and 60% levels by weight. Two phytase-producing bacterial strains, Bacillus cereus LRF5 and Staphylococcus caprae CCF2 isolated from the gut of adult Labeo rohita and Catla catla, respectively were used for fermentation of SBM. Fermentation of SBM was effective in reducing the anti-nutritional factors, trypsin inhibitor and phytic acid and enhancing protein, lipid and mineral concentration. The response of L. rohita, fingerlings (initial weight 3.33±0.07 g) fed the experimental diets for 100 days was compared with fish fed a fish meal based diet. In terms of growth, feed conversion ratio and protein efficiency ratio, diet S7 containing 45% SBM fermented with B. cereus LRF5 resulted in a significantly (P<0.05) better performance of fish. The overall performance of L. rohita fed fermented SBM incorporated diets was better in comparison to those fed raw SBM incorporated diets. The apparent digestibility of nutrients and minerals was significantly (P<0.05) higher in fish fed diet S7. The maximum deposition of protein in the carcass was recorded in fish fed diet S7. Diets containing fermented SBM reduced fecal P levels.&nbsp

A Comprehensive Review of Magnetoencephalography (MEG) Studies for Brain Functionality in Healthy Aging and Alzheimer's Disease (AD)

Neural oscillations were established with their association with neurophysiological activities and the altered rhythmic patterns are believed to be linked directly to the progression of cognitive decline. Magnetoencephalography (MEG) is a non-invasive technique to record such neuronal activity due to excellent temporal and fair amount of spatial resolution. Single channel, connectivity as well as brain network analysis using MEG data in resting state and task-based experiments were analyzed from existing literature. Single channel analysis studies reported a less complex, more regular and predictable oscillations in Alzheimer's disease (AD) primarily in the left parietal, temporal and occipital regions. Investigations on both functional connectivity (FC) and effective (EC) connectivity analysis demonstrated a loss of connectivity in AD compared to healthy control (HC) subjects found in higher frequency bands. It has been reported from multiplex network of MEG study in AD in the affected regions of hippocampus, posterior default mode network (DMN) and occipital areas, however, conclusions cannot be drawn due to limited availability of clinical literature. Potential utilization of high spatial resolution in MEG likely to provide information related to in-depth brain functioning and underlying factors responsible for changes in neuronal waves in AD. This review is a comprehensive report to investigate diagnostic biomarkers for AD may be identified by from MEG data. It is also important to note that MEG data can also be utilized for the same pursuit in combination with other imaging modalities

Formulation Development to Enhance the Solubility of Metoclopramide Base Drug by Solid Dispersion and Evaluation of Transdermal Film

Aims & Objectives: The present work deals with the modification of controlled release dosage form of poorly water soluble drug (Metoclopramide hydrochloride) in order to improve the bioavailability and to control drug release for a longer period of time by the aid of solid dispersion. Methods: Various binary combination of MET-solid dispersion was prepared with different carriers such as HPβCD, PVP K30 and PLX-188 by solvent evaporation technique and then the aqueous solubility, dissolution study and phase solubility study was performed. DSC analysis is performed to carry out for metoclopramide loaded solid dispersion, physical mixture & also for pure drug to analyze the crystalline and amorphous nature of compounds. Results and Discussion:  The saturation solubility of Metoclopramide with various carriers at different pH was performed and found that in pH 5.5 (solubility is 5553.2µg/ml), pH 6.8 (3363.3µ/ml), pH 7.4 (1367.3µg/ml) at 37oC. In dissolution study of solid dispersion (5:1) of different carriers in DDW, the Cumulative % dissolution is found in the order of PVP K30>PLX-Met>HPβCD-Met & in pH 7.4, in the order of PLX-Met>PVP K30>HPβCD-Met. DSC thermogram of Metoclopramide base showed a sharp endothermic peak at its melting point (147oC) which exhibits in crystalline form complying with that of Metoclopramide hydrochloride form, melting point was found to be 850C.  In the ex-vivo study of several transdermal patches, patch C [SD of MET: HPβCD (1:5)] showed the controlled release and permeation of drug. Conclusion: Poor solubility of new chemical entities being a well known problem for past few decades despite the imbalance between significant research efforts & few successful marketed formulations, the solid dispersion proves to hold a key position among all the various formulation strategies to enhance the aqueous solubility & dissolution rate and thereby the bioavailability of  poorly aqueous solubility of drug. Keywords: Bioavailability,DSC, Metoclopramide hydrochloride, solid dispersion, HPβCD

Common variants in CLDN2 and MORC4 genes confer disease susceptibility in patients with chronic pancreatitis

A recent Genome-wide Association Study (GWAS) identified association with variants in X-linked CLDN2 and MORC4 and PRSS1-PRSS2 loci with Chronic Pancreatitis (CP) in North American patients of European ancestry. We selected 9 variants from the reported GWAS and replicated the association with CP in Indian patients by genotyping 1807 unrelated Indians of Indo-European ethnicity, including 519 patients with CP and 1288 controls. The etiology of CP was idiopathic in 83.62% and alcoholic in 16.38% of 519 patients. Our study confirmed a significant association of 2 variants in CLDN2 gene (rs4409525—OR 1.71, P = 1.38 x 10-09; rs12008279—OR 1.56, P = 1.53 x 10-04) and 2 variants in MORC4 gene (rs12688220—OR 1.72, P = 9.20 x 10-09; rs6622126—OR 1.75, P = 4.04x10-05) in Indian patients with CP. We also found significant association at PRSS1-PRSS2 locus (OR 0.60; P = 9.92 x 10-06) and SAMD12-TNFRSF11B (OR 0.49, 95% CI [0.31–0.78], P = 0.0027). A variant in the gene MORC4 (rs12688220) showed significant interaction with alcohol (OR for homozygous and heterozygous risk allele -14.62 and 1.51 respectively, P = 0.0068) suggesting gene-environment interaction. A combined analysis of the genes CLDN2 and MORC4 based on an effective risk allele score revealed a higher percentage of individuals homozygous for the risk allele in CP cases with 5.09 fold enhanced risk in individuals with 7 or more effective risk alleles compared with individuals with 3 or less risk alleles (P = 1.88 x 10-14). Genetic variants in CLDN2 and MORC4 genes were associated with CP in Indian patients