Evaluating classification tools for the prediction of in-vitro microbial pyruvate yield from organic carbon sources.

The laboratory-scale (in-vitro) microbial fermentation based on screening of process parameters (factors) and statistical validation of parameters (responses) using regression analysis. The recent trends have shifted from full factorial design towards more complex response surface methodology designs such as Box-Behnken design, Central Composite design. Apart from the optimisation methodologies, the listed designs are not flexible enough in deducing properties of parameters in terms of class variables. Machine learning algorithms have unique visualisations for the dataset presented with appropriate learning algorithms. The classification algorithms cannot be applied on all datasets and selection of classifier is essential in this regard. To resolve this issue, factor-response relationship needs to be evaluated as dataset and subsequent preprocessing could lead to appropriate results. The aim of the current study was to investigate the data-mining accuracy on the dataset developed using in-vitro pyruvate production using organic sources for the first time. The attributes were subjected to comparative classification on various classifiers and based on accuracy, multilayer perceptron (neural network algorithm) was selected as classifier. As per the results, the model showed significant results for prediction of classes and a good fit. The learning curve developed also showed the datasets converging and were linearly separable.

Development of machine learning algorithm for assessment of heat transfer of ternary hybrid nanofluid flow towards three different geometries: Case of artificial neural network.

The focus of this paper revolves around the examination of flow of ternary hybrid nanofluid, specifically the Al2O3-Cu-CNT/water mixture, with buoyancy effect, across three distinct geometries: a wedge, a flat plate, and a cone. The study takes into account the presence of quadratic thermal radiation and heat source/sink of non-uniform nature. To develop the model, the Cattaneo-Christov theory is utilized. The equations governing the flow are solved by applying similarity transformations and employing the "bvp4c function in MATLAB" for numerical analysis and solution. Conventional methods for conducting parametric studies often face challenges in producing significant conclusions owing to the inherent complex form of the model and the method involved. To address the aforementioned issue, this paper explores the potential of machine learning methods to foresee the conduct of the flow characterized by multiple interconnected parameters. By utilizing simulated data, an artificial neural network is trained using the Levenberg-Marquardt algorithm to learn and comprehend the underlying patterns. Subsequently, the trained neural network is employed to estimate the Nusselt number on the surfaces of all three geometries. This approach offers a promising alternative to traditional parametric studies, enabling more precise predictions and insights into the behavior of complex systems. The Nusselt number is highest for THNF flow over the cone. The mean squared error (MSE) values for the ANN algorithm, across all analyzed cases, range from 0 to 0.03972. The findings contribute to an improved understanding of the characteristics and dynamics of ternary hybrid nanofluid flow in various geometries, assisting in the design and optimization of heat transfer systems involving such fluids.

Evaluation of the inhibitory potential of Valproic acid against histone deacetylase of Leishmania donovani and computational studies of Valproic acid derivatives.

Valproic acid (VA) is a proven inhibitor of human histone deacetylases (HDACs). The homogenous HDAC has been associated with all major human parasitic pathogens and hence, it has been considered an attractive drug target for anti-leishmanial therapy. To assist in drug design endeavors for HDACs, an in-vitro study has been presented to investigate the VA inhibition on Leishmania donovani HDAC (LdHDAC). The regression analysis of VA by 24 hrs viability assay confirmed its activity against LdHDAC. Moreover, the toxicity of VA is also well documented. Thus, the in-silico experiments were also conducted to screen the non-toxic VA derivatives as anti-leishmanial drug candidates having potential as inhibitors of LdHDAC. For in-silico study, the 3D structure of target LdHDAC was developed by homology modeling. Based on their in-silico activity, we shortlisted 13 VA derivatives having maximum affinity for LdHDAC and identified four potential derivatives that can specifically bind to this protein. After that, these ligands were subjected to molecular dynamics simulation. These derivatives may be effective against L. donovani promastigotes since they followed Lipinski's RO5 and were non-toxic. Thus, screened derivatives can be considered as lead ligands for targeting LdHDAC and may be used as possible drug candidates to treat leishmaniasis and overcome the limitation of anti-leishmanial drugs. This is the first report of antileishmanial potential of VA and its derivatives targeting LdHDAC. Hence, the current investigation presents a search for novel target specific drugs to aid the anti-leishmanial drug development.Communicated by Ramaswamy H. Sarma.

Identification of essential oil phytocompounds as natural inhibitors of Odorant-binding protein to prevent malaria through in silico approach.

Malaria predominantly affects millions annually in the African and Asian tropical and subtropical countries. With no effective vaccine, malaria prevention is exclusively dependent on preventing human-vector interaction. Anopheles gambiae, the main vector of the malaria parasite Plasmodium falciparum contains Odorant Binding proteins (OBPs) which are considered an attractive drug target for anti-malarial therapy. To identify a potential anti-malarial compound, we performed a structure-based screening of 876 phytocompounds derived from essential oils against the OBP4 by molecular docking. The compounds having better docking scores were assessed for drug-likeness, toxicity, and molecular interaction analysis. As per the results, strong affinities and high stability were demonstrated by two phytocompounds viz. Alpha-cyperone (-8.1 kcal mol-1) and Humulene oxide (-8.1 kcal mol-1) with OBP4. The hydrophobic interactions involve Phe123, Ala106, Thr57, Ala52, Thr69, and Ile64 within the binding cavities, which may block the OBP4 receptor resulting in disorientation. After that, the potential compounds were subjected to molecular dynamics (MD) simulation to evaluate their structural stability and dynamics at the active site of OBP4. The MM-PBSA result revealed that Alpha-cyperone and Humulene oxide had binding free energy of -92.44 kJ mol-1 and -113.25 kJ mol-1, respectively. Simulation outcomes demonstrate that these phytocompounds displayed considerable significant structural and pharmacological properties. The LD50 value of Alpha-cyperone and Humulene oxide also suggested that both are safe and suitable for use in natural repellent development. We suggest that the use of these compounds can minimize the treatment period and the various side effects associated with the currently available anti-malarial drugs.Communicated by Ramaswamy H. Sarma.

Identification of SARS-CoV-2 RNA dependent RNA polymerase inhibitors using pharmacophore modelling, molecular docking and molecular dynamics simulation approaches.

The RNA-dependent RNA polymerase (RdRp) is one of the crucial enzymes in severe acute respiratory syndrome Coronavirus-2 (SARS-CoV-2) catalysing the replication of RNA, therefore acts as a potential target for antiviral drug design. The fixation of a ligand in the active site of RdRp may alter the SARS-CoV-2 life cycle. Present work aimed at identifying novel inhibitors of the SARS-CoV-2 RdRp enzyme by performing pharmacophore-based virtual screening, molecular docking and molecular dynamics simulation (MDS). Initially, the pharmacophore model of SARS-CoV-2 RdRp was constructed and the resulting model was used to screen compounds from ChEMBL, ZINC and PubChem databases. During the investigation, 180 compounds were screened using the above model and subjected to molecular docking with RdRp. Two compounds viz. ChEMBL1276156 and PubChem135548348 showed a strong binding affinity with RdRp than its standard inhibitor, Remdesivir. Toxicity prediction of these two compounds reveals their non-toxic nature. These compounds were further subjected to MDS for 100 ns to check their stability after binding with RdRp. The MDS of RdRp-ChEMBL1276156 and RdRp-PubChem135548348 complexes show enhanced stability in comparison to the RdRp-Remdesivir complex. The average interaction energy calculated after 100 ns of MDS was -146.56 and -172.68 KJ mol-1 for RdRp-CHEMBL1276156 complex and RdRp-PubChem135548348 complex, respectively, while -59.90 KJ mol-1 for RdRp-Remdesivir complex. The current investigation reveals that these two compounds are potent inhibitors of SARS-CoV-2 RdRp and they could be tested in the experimental condition to evaluate their efficacy against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Cases of Left Against Medical Advice from the Emergency Department of a Tertiary Care Hospital in Kathmandu: A Descriptive Cross-Sectional Study.

INTRODUCTION: Left against medical advice is a worldwide phenomenon. Patients leaving against Left against medical advice do not provide the health professionals with legal impunity. A well-informed consent should be present with surety that they are well understood by the patient before they leave. The study was undertaken to study the prevalence of patients that leave against medical advice. METHODS: This is a descriptive cross-sectional study done in the emergency department of a tertiary care hospital from 1st February 2020 to 31 July 2020. Ethical approval was taken from the Institutional Review Committee (ref. no. 130120205). The sample size was calculated and the convenient sampling method was used. Data were analyzed in the Statistical Package of the Social Sciences version 22. Point estimate at 95% Confidence Interval was calculated along with frequency and proportion for binary data. RESULTS: Out of 5834 visits, 332 (5.96%) (4.70-7.22 at 95% Confidence Interval) patients left against medical advice. The mean age was 36.48 years (3 days-91 years) and males 173 (52.3%) were prone to leave than females. Only 50 (15.1%) cases had well-informed consent with complications documented. Hundred (30.5%) patients had wanted to come on follow up the next day in the out-patient department while 41 (12.4%) had to leave because of financial reasons. Only seven (2.9%) of well-oriented patients gave their consent and the remaining 233 (97.1%) were by the kin present. Only 76 (23%) patients were sent home with a well-documented medicine prescription. CONCLUSIONS: The proportion of patients who left against medical advice was more than the studies done in a similar setting.

Current costs & projected financial needs of India's Universal Immunization Programme.

BACKGROUND & OBJECTIVES: India's Universal Immunization Programme (UIP) is one of the largest programmes in the world in terms of quantities of vaccines administered, number of beneficiaries, number of immunization sessions, and geographical extent and diversity of areas covered. Strategic planning for the Programme requires credible information on the cost of achieving the objectives and the financial resources needed at national, State, and district levels. We present here expenditures on immunization services in India in 2012 (baseline) and projected costs for five years (2013-2017). METHODS: Data were collected from the Immunization Division of the Ministry of Health and Family Welfare, Government of India, and immunization partners, such as the World Health Organization and UNICEF. The cost components were immunization personnel, vaccines and injection supplies, transportation, trainings, social mobilization, advocacy and communication activities, disease surveillance, Programme management, maintenance of cold chain and other equipment, and capital costs. RESULTS: Total baseline expenditure was ₹ 3,446 crore [1 crore = 10 million] (US$718 million), including shared personnel costs. In 2012, the government paid for 90 per cent of the Programme. Total resource requirements for 2013-2017 are ₹ 34,336 crore (US$ 5, 282 million). Allocations for vaccines increase from ₹ 511 crore in 2013 to ₹ 3,587 crore in 2017 as new vaccines are assumed to be introduced in the Programme. INTERPRETATION & CONCLUSIONS: The projections show that the government immunization budget will be double in 2017 as compared to 2013. It will increase from ₹ 4,570 crore in 2013 to ₹ 9,451 crore in 2017.

Advances in systemic therapies for metastatic castration-resistant prostate cancer.

The landscape of treatment for metastatic castration-resistant prostate cancer has rapidly evolved during the last 5 years alone. In this review, standard therapies as well as recent advances in the systemic treatment for prostate cancer are explored. Pivotal trial data are summarized with emphasis on indications for various anti-androgen drugs, androgen-biosynthesis inhibitors, chemotherapy, immunotherapy and bone-targeted agents. The clinical, biochemical and radiographic outcomes for men with metastatic castration-resistant prostate cancer are improving with the establishment of several promising novel agents.

Lymphocyte-predominant Hodgkin disease: a comprehensive overview.

Lymphocyte-predominant Hodgkin disease is a rare form of Hodgkin lymphoma that is recognized as a separate histopathological entity. This disease tends to have multiple relapses, but has an overall good prognosis. Owing to its rarity, and the prolonged time period between recurrence and transformation events, there is no consensus regarding optimal management. However, the National Comprehensive Cancer Network guidelines indicate that for early stages, appropriate treatment is radiotherapy. Several management options have been reported including observation, radiation, chemotherapy, combined chemoradiotherapy, and anti-CD20 antibody therapy. Salvage therapy remains effective in inducing prolonged remission in patients with relapsed/refractory disease.

Is medial temporal injury responsible for pediatric postconcussion syndrome? A prospective controlled study with single-photon emission computerized tomography.

OBJECT: The authors sought to correlate the finding of medial temporal hypoperfusion (MTH) demonstrated on single photon emission computerized tomography (SPECT) with pediatric persistent postconcussion syndrome (PPCS) and to ascertain its usefulness in routine clinical practice. METHODS: Thirty consecutive children with minor head injury and features of PPCS underwent SPECT scanning within 72 hours of injury. Those children having MTH on SPECT were included in the test group (14 patients), and the remaining 16 children comprised the control group. At the end of a 3-month period SPECT scanning was repeated and the incidence of PPCS was assessed clinically in both groups. Repeated SPECT scanning at 3 months revealed persisting MTH in 13 children (93%) in the test group; no child developed MTH in the control group. Twelve children were found to have PPCS in the MTH group compared with only two in the control group, and this was highly statistically significant (relative risk 6.86 [95% confidence interval 1.84-25.51], p = 0.0003). CONCLUSIONS: There exists significant MTH in pediatric patients with PPCS, which would imply that medial temporal lobe damage (involving the hippocampus and related structures) may occur after minor head injury and could be responsible for the symptoms of PPCS observed in this group of patients. Brain SPECT scans may thus help in the early identification of children prone to develop PPCS, and serial SPECT scanning may serve as a platform for testing the efficacy of various neurobehavioral and pharmacological interventions in these patients.

Phosphorylation Activity in the Alzheimer's Disease and Normal Brain is Modulated by Microtubule-Associated Protein, Tau in Vitro.

One of the hallmarks of Alzheimer's Disease is the presence of abundant neurofibrillary tangles (NFTs) in the brains of affected individuals. Hyperphosphorylated tau is a major component of paired helical filaments (PHFs) in NFTs. Tau is a neuronal microtubule associated protein found primarily in axons. Normal tau promotes tubulin polymerization and stabilizes microtubule (MT) structures, whereas hyperphosphorylated tau reduces its affinity for MTs and destabilizes MT-structures. This results in the disruption of vital cellular processes (e.g. axonal transport) and leads to the degeneration of affected neurons. Processes leading to the hyperphosphorylation of tau and formation of neurofibrillary lesions in Alzheimer's Disease (AD) brains are not understood. Phosphorylation of a substrate molecule like tau depends upon the equilibrium between kinase and phosphatase activities and the availability of their substrate molecules in a given system. Therefore, to understand the relative roles of kinase and phosphatase activities, we studied the long-term kinetics of phosphorylation in AD and control brain extracts in the presence and absence of the phosphatase inhibitor okadaic acid (OA) using histone, casein and bacterially expressed tau as exogenous substrates. It was found that both kinase and phosphatase activities were higher in AD compared to control brains. Surprisingly, between 18 and 24 hours, there was a robust increase in phosphorylation of endogenous proteins in the brain extracts only when bacterially expressed tau was present in the phosphorylation reaction mixture. This pattern of phosphorylation activity was unaffected by OA. Significant difference in the phosphorylation of tau isoforms was also seen during this period. These data suggest that the expression and differential phosphorylation of certain tau isoforms may be responsible for the robust increase in phosphorylation and may play an important role in Alzheimer's pathology.