Biosorption of arsenic (III) from aqueous solution using calcium alginate immobilized dead biomass of Acinetobacter sp. strain Sp2b.

This study presents a novel biosorbent developed by immobilizing dead Sp2b bacterial biomass into calcium alginate (CASp2b) to efficiently remove arsenic (AsIII) from contaminated water. The bacterium Sp2b was isolated from arsenic-contaminated industrial soil of Punjab, a state in India. The strain was designated Acinetobacter sp. strain Sp2b as per the 16S rDNA sequencing, GenBank accession number -OP010048.The CASp2b was used for the biosorption studies after an initial screening for the biosorption capacity of Sp2b biomass with immobilized biomass in both live and dead states. The optimum biosorption conditions were examined in batch experimentations with contact time, pH, biomass, temperature, and AsIII concentration variables. The maximum biosorption capacity (qmax = 20.1 ± 0.76 mg/g of CA Sp2b) was obtained at pH9, 35 ̊ C, 20 min contact time, and 120 rpm agitation speed. The isotherm, kinetic and thermodynamic modeling of the experimental data favored Freundlich isotherm (R2 = 0.941) and pseudo-2nd-order kinetics (R2 = 0.968) with endothermic nature (ΔH° = 27.42) and high randomness (ΔS° = 58.1).The scanning electron microscopy with energy dispersive X-ray (SEM-EDX) analysis indicated the As surface binding. The reusability study revealed the reasonable usage of beads up to 5 cycles. In conclusion, CASp2b is a promising, efficient, eco-friendly biosorbent for AsIII removal from contaminated water.

Role of infrastructure and operation in disease prevalence in dairy farms: groundwork for disease prevention-based antibiotic stewardship.

Attempts at regulating misuse of antibiotics in the dairy industry have been ineffective, especially in low- and middle-income countries, who also typically have high burden of preventable infectious disease, we propose a disease prevention-based approach to minimize the need and in turn consumption of antibiotics in dairy farms. Since the immediate environment of the animals is key to disease prevalence, we targeted the infrastructure- and operation-related factors in dairy farms and their link with prevalence of most common diseases and symptoms. We conducted four focused group discussions and a cross-sectional survey in 378 dairy farms to investigate disease prevalence and associated infrastructural (housing system, and manger shape), and operational (waste management, feed management, and type of cleaning agent) parameters. The most common diseases (Mastitis and secondary infections related to Foot-and-mouth disease) and symptoms (fever and diarrhoea) in the focus area were linked with the infrastructural and operational factors on the dairy farm with higher disease prevalence reported in dairy farms, where the animals were exposed to variations in diurnal temperatures or were hard to clean. We further used ML classifiers - Neural Network (NN), k-Nearest Neighbour (kNN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) - to corroborate the relationship between infrastructure and operations of the dairy farms and disease prevalence- The DT classifier on randomly sampled data could predict the prevalence of the two most common diseases (accuracy = 92%, F1-score = 0.919) Our results open new avenues for cost-effective interventions such as use of curve-edged mangers, use of rubber mats on floors, not reusing leftover feed etc. in dairy farms to prevent the most common diseases and symptoms in dairy farms and reduce the need and consumption of antibiotics.

Homeopathic Medicines in Third (Omicron) Wave of COVID-19: Prognostic Factor Research.

BACKGROUND: With the emergence of new variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, such as the Omicron variant, during the third wave of the coronavirus disease 2019 (COVID-19) pandemic, there was a need to identify useful homeopathic medicines. This study aimed to identify such medicines and their indications using prognostic factor research (PFR). METHODS: This was an open-label, multi-centred observational study conducted in January 2022, on confirmed COVID-19 cases. The data were collected from integrated COVID Care Centres in Delhi, India, where homeopathic medicines were prescribed along with conventional treatment. Only those cases that met a set of selection criteria were considered for analysis. The likelihood ratio (LR) was calculated for the frequently occurring symptoms of the frequently prescribed medicines. An LR of 1.3 or greater was considered meaningful. RESULTS: Out of the 362 COVID-19 cases, 263 cases were selected for analysis after applying selection criteria. Common symptoms included fatigue, cough, sore throat, myalgia and headache. Twenty-one medicines were prescribed, of which nine medicines - Gelsemium sempervirens, Bryonia alba, Hepar sulphuris, Rhus toxicodendron, Pulsatilla nigricans, Arsenicum album, Belladonna, Nux vomica and Phosphorus - were frequently used. By calculating LRs, the study identified meaningful indications for these medicines. CONCLUSION: Homeopathic medicines have shown promising results in the third wave of COVID-19 as an adjunct therapy. The medicines that were used in the first and second waves were found useful in the third wave also, and their indications were analogous to those found in the earlier waves. Certain new indications of some medicines were elicited in this wave, which warrant further research. However, it is important not to restrict to these medicines only and to continue data collection on COVID-19 in future waves for the improvement of the COVID-19 mini-repertory.

Effect of Arsenic on Fluoride Tolerance in Microbacterium paraoxydans Strain IR-1.

Fluoride (F) and arsenic (As) are two major contaminants of water and soil systems around the globe, causing potential toxicity to humans, plants, animals, and microbes. These contaminated soil systems can be restored by microorganisms that can tolerate toxic stress and provide rapid mineralization of soil, organic matter, and contaminants, using various tolerance mechanisms. Thus, the present study was undertaken with the arsenic hyper-tolerant bacterium Microbacterium paraoxydans strain IR-1 to determine its tolerance and toxicity to increasing doses of fluoride, either individually or in combination with arsenic, in terms of growth inhibition using a toxicity unit model. The minimum inhibitory concentration (MIC)and half maximal inhibitory concentration (IC50) values for fluoride increased, from 9 g/L to 11 g/L and from 5.91 ± 0.1 g/L to 6.32 ± 0.028 g/L, respectively, in the combination (F + As) group. The statistical comparison of observed and expected additive toxicities, with respect to toxicity unit (TU difference), using Student's t-test, was found to be highly significant (p < 0.001). This suggests the antagonistic effect of arsenic on fluoride toxicity to the strain IR-1. The unique stress tolerance of IR-1 ensures its survival as well as preponderance in fluoride and arsenic co-contaminated sites, thus paving the way for its possible application in the natural or artificial remediation of toxicant-exposed degraded soil systems.

Understanding and predicting future relapse in depression from resting state functional connectivity and self-referential processing.

BACKGROUND: The recurrent nature of Major Depressive Disorder (MDD) asks for a better understanding of mechanisms underlying relapse. Previously, self-referential processing abnormalities have been linked to vulnerability for relapse. We investigated whether abnormalities in self-referential cognitions and functioning of associated brain-networks persist upon remission and predict relapse. METHODS: Remitted recurrent MDD patients (n = 48) and never-depressed controls (n = 23) underwent resting-state fMRI scanning at baseline and were additionally assessed for their implicit depressed self-associations and ruminative behaviour. A template-based dual regression approach was used to investigate between-group differences in default mode, cingulo-opercular and frontoparietal network resting-state functional connectivity (RSFC). Additional prediction of relapse status at 18-month follow-up was investigated within patients using both regression analyses and machine learning classifiers. RESULTS: Remitted patients showed higher rumination, but no implicit depressed self-associations or RSFC abnormalities were observed between patients and controls. Nevertheless, relapse was related to i) baseline RSFC between the ventral default mode network and the precuneus, dorsomedial frontal gyrus, and inferior occipital lobe, ii) implicit self-associations, and iii) uncontrollability of ruminative thinking, when controlled for depressive symptomatology. Moreover, preliminary machine learning classifiers demonstrated that RSFC within the investigated networks predicted relapse on an individual basis. CONCLUSIONS: Remitted MDD patients seem to be commonly characterized by abnormal rumination, but not by implicit self-associations or abnormalities in relevant brain networks. Nevertheless, relapse was predicted by self-related cognitions and default mode RSFC during remission, suggesting that variations in self-relevant processing play a role in the complex dynamics associated with the vulnerability to developing recurrent depressive episodes. CLINICAL TRIAL REGISTRATION: Netherlands Trial Register, August 18, 2015, trial number NL53205.042.15.

Comparing resting state and task-based EEG using machine learning to predict vulnerability to depression in a non-clinical population.

Major Depressive Disorder (MDD) affects a large portion of the population and levies a huge societal burden. It has serious consequences like decreased productivity and reduced quality of life, hence there is considerable interest in understanding and predicting it. As it is a mental disorder, neural measures like EEG are used to study and understand its underlying mechanisms. However most of these studies have either explored resting state EEG (rs-EEG) data or task-based EEG data but not both, we seek to compare their respective efficacy. We work with data from non-clinically depressed individuals who score higher and lower on the depression scale and hence are more and less vulnerable to depression, respectively. Forty participants volunteered for the study. Questionnaires and EEG data were collected from participants. We found that people who are more vulnerable to depression had on average increased EEG amplitude in the left frontal channel, and decreased amplitude in the right frontal and occipital channels for raw data (rs-EEG). Task-based EEG data from a sustained attention to response task used to measure spontaneous thinking, an increased EEG amplitude in the central part of the brain for individuals with low vulnerability and an increased EEG amplitude in right temporal, occipital and parietal regions in individuals more vulnerable to depression were found. In an attempt to predict vulnerability (high/low) to depression, we found that a Long Short Term Memory model gave the maximum accuracy of 91.42% in delta wave for task-based data whereas 1D-Convolution neural network gave the maximum accuracy of 98.06% corresponding to raw rs-EEG data. Hence if one has to look at the primary question of which data will be good for predicting vulnerability to depression, rs-EEG seems to be better than task-based EEG data. However, if mechanisms driving depression like rumination or stickiness are to be understood, task-based data may be more effective. Furthermore, as there is no consensus as to which biomarker of rs-EEG is more effective in the detection of MDD, we also experimented with evolutionary algorithms to find the most informative subset of these biomarkers. Higuchi fractal dimension, phase lag index, correlation and coherence features were also found to be the most important features for predicting vulnerability to depression using rs-EEG. These findings bring up new possibilities for EEG-based machine/deep learning diagnostics in the future.

A Systematic Review on Interferon Alpha-Induced Cognitive Impairment in Hepatitis C Patients.

OBJECTIVE: Interferon-alpha (IFN-α) is an important treatment modality for the hepatitis C virus (HCV). However, treatment with IFN-α is often associated with cognitive difficulties in HCV patients. Thus, this systematic review was performed to assess the effects of IFN-α on cognitive functioning in patients suffering from HCV. METHODS: Relevant literature was identified by performing a comprehensive literature search in major databases including PubMed, clinicaltrials.gov, and Cochrane Central using a combination of suitable keywords. We retrieved studies that were published from the start of each database until August 2021. RESULTS: Out of 210 articles, 73 studies were selected after removing the duplicates. In the first pass, 60 articles were excluded. Out of 13 full-text articles, only 5 articles qualified for qualitative analyses in the second pass. We observed conflicting results concerned with the use of IFN-α and the risk of neurocognitive impairment in HCV patients. CONCLUSION: In conclusion, we have observed conflicting results regarding the impact of INF-α treatment on the cognitive functioning of patients suffering from HCV. Thus, there is an urgent need for an extensive study to evaluate the exact association between INF-αtherapy and cognitive functioning in HCV patients.

Decoding the cognitive states of attention and distraction in a real-life setting using EEG.

Lapses in attention can have serious consequences in situations such as driving a car, hence there is considerable interest in tracking it using neural measures. However, as most of these studies have been done in highly controlled and artificial laboratory settings, we want to explore whether it is also possible to determine attention and distraction using electroencephalogram (EEG) data collected in a natural setting using machine/deep learning. 24 participants volunteered for the study. Data were collected from pairs of participants simultaneously while they engaged in Tibetan Monastic debate, a practice that is interesting because it is a real-life situation that generates substantial variability in attention states. We found that attention was on average associated with increased left frontal alpha, increased left parietal theta, and decreased central delta compared to distraction. In an attempt to predict attention and distraction, we found that a Long Short Term Memory model classified attention and distraction with maximum accuracy of 95.86% and 95.4% corresponding to delta and theta waves respectively. This study demonstrates that EEG data collected in a real-life setting can be used to predict attention states in participants with good accuracy, opening doors for developing Brain-Computer Interfaces that track attention in real-time using data extracted in daily life settings, rendering them much more usable.

Prevalence of E. coli O157:H7 in water sources: an overview on associated diseases, outbreaks and detection methods.

Escherichia coli O157:H7 is a zoonotic pathogen with its ability to cause human illness ranging from diarrheal disease to fatal hemolytic uremic syndrome. E. coli O157:H7 had been associated with waterborne outbreaks resulting in high morbidity and mortality worldwide. Therefore, it is important to investigate the prevalence of E. coli O157:H7 in water sources especially used for drinking and to develop the diagnostic methods for its early detection. The review describes traditional cultural methods, immunological techniques, and polymerase chain reaction (PCR)-based methods for detection of this bacterium in water sources. The current PCR-based techniques such as real-time PCR are more specific and sensitive and require less detection time (<3 hours). These methods can be applied for regular water monitoring and proper management of water sources to prevent waterborne diseases due to E. coli O157:H7.

Arsenic Hyper-tolerance in Four Microbacterium Species Isolated from Soil Contaminated with Textile Effluent.

Arsenic-contaminated areas of Sanganer, Jaipur, Rajasthan, India were surveyed for the presence of metal resistant bacteria contaminated with textile effluent. Samples were collected from soil receiving regular effluent from the textile industries located at Sanganer area. The properties like pH, electrical conductivity, organic carbon, organic matter, exchangeable calcium, water holding capacity and metals like arsenic, iron, magnesium, lead and zinc were estimated in the contaminated soil. In total, nine bacterial strains were isolated which exhibited minimum inhibitory concentration (MIC) of arsenic ranging between 23.09 and 69.2mM. Four out of nine arsenic contaminated soil samples exhibited the presence of arsenite hyper-tolerant bacteria. Four high arsenite tolerant bacteria were characterized by 16S rDNA gene sequencing which revealed their similarity to Microbacterium paraoxydans strain 3109, Microbacterium paraoxydans strain CF36, Microbacterium sp. CQ0110Y, Microbacterium sp. GE1017. The above results were confirmed as per Bergey's Manual of Determinative Bacteriology. All the four Microbacterium strains were found to be resistant to 100µg/ml concentration of cobalt, nickel, zinc, chromium selenium and stannous and also exhibited variable sensitivity to mercury, cadmium, lead and antimony. These results indicate that the arsenic polluted soil harbors arsenite hyper-tolerant bacteria like Microbacterium which might play a role in bioremediation of the soil.

Anticancer and antimutagenic properties of Acacia nilotica (Linn.) on 7,12-dimethylbenz(a)anthracene-induced skin papillomagenesis in Swiss albino mice.

We report the chemopreventive activity of Acacia nilotica (Linn.) gum, flower and leaf aqueous extracts, on 7,12-dimethylbenz(a)anthracene (DMBA) induced skin papillomagenesis in male Swiss albino mice. Animals were divided into following groups: Group I (Controls) given DMBA and croton oil, with no extract ; Group II (treatment) animals treated with Acacia nilotica gum (Group II-a) (800 mg/kg body weight), flowers (Group II-b) (800 mg/kg body weight), or leaves (Group II-c) (800 mg/kg body weight) during the peri- and post initiation periods of DMBA and croton oil application. A significant reduction in the values of tumor burden, tumor incidence and cumulative number of papillomas was observed in mice treated by oral gavage with the Acacia nilotica gum, flower and leaf extracts as compared with the control group. The latency period in treatment Group-II (b) and Group-II (c) was significantly increased as compared with the control group. A significant reduction in the frequency of micronuclei was also observed in mice treated by oral gavage with the aqueous extracts, along with significant decrease in total chromosomal aberrations in the form of chromatid breaks, chromosome breaks, centric rings, dicentrics, acentric fragments and exchange. Treatment with Acacia nilotica flower (Group II-B) and leaf (Group II-C) aqueous extracts by oral gavage for 15 days resulted in a highly significant decrease in the lipid peroxidation (LPO) level in the liver, but this was less evident with the gum (Group II-A) . Conversely, reduced glutathione (GSH) content was observed to be significantly elevated as compared with the control group with leaves (Group II-C) and flowers (Group II-B). The chemopreventive and antimutagenic activity of the leaf extract of Acacia nilotica was most significant followed by the flower extract and then by gum.