Differential expression of miRNAs and related mRNAs during heat stress in buffalo heifers.

The present experiment was aimed to study differential expression of miRNAs and related mRNAs during heat stress (HS) in buffalo heifers. Twelve Murrah buffalo heifers aged between 1.5 and 2.0 years, weighting between 250 and 300 Kg were randomly assigned into two equal groups. The animals were kept in the psychrometric chamber under Thermo-neutral (TN; THI = 72) and HS (THI = 87-90) conditions for 6 h every day between 1000 and 1600 h for 21 days. The blood sampling was done at 1500 h on 15th day of the experiment and physiological parameters viz. pulse rate (PR), respiratory rate (RR) and rectal temperature (RT) were recorded at 1500 h on day -5, -3, -1, 0, +1, +3, +5 with respect to blood sampling. PBMCs were used for extraction of miRNAs and total RNA; and first strand cDNA was synthesized. qPCR was performed for relative gene expression studies. Physiological, hematological (erythrocytic indices), biochemical (triglycerides, urea, ALT, AST, LDH), redox (SOD, ROS) and endocrine parameters (T4) altered significantly (P < 0.05) during HS as compared to TN. Out of eight targeted miRNAs only four were expressed in buffalo heifers. The relative expression of bta-mir-142, bta-mir-1248 and bta-mir-2332 was significantly (P < 0.05) up-regulated whereas expression of bta-mir-2478 was significantly (P < 0.05) down-regulated during HS as compared to TN. The relative expression of the predicted target genes i.e. HSF1, HSP60, HSP70, HSPA8 and HSP90 were significantly (P < 0.05) up-regulated whereas HSF4 expression was significantly (P < 0.05) down-regulated during HS as compared to TN. It can be concluded that a THI of 87-90 could lead to a moderate HS in buffalo heifers. Differential expression studies of miRNAs and related mRNAs in present study deciphers the role of miRNAs in the heat tolerance in buffalo heifers.

Appropriate THI model and its threshold for goats in semi-arid regions of India.

The present study was attempted to identify an appropriate THI model and threshold THI for goats of semi-arid regions of India. Sixty non-pregnant goats each from Jamunapari and Barbari breeds were selected for the study. The study was conducted from last week of February to first week of June, during which average THI ranged between 53 and 92. Pulse rate (PR), respiration rate (RR) and rectal temperature (RT) were recorded at 1430 h on alternate days from six goats of each breed randomly during the experiment. Nine THI models were used to calculate THI. An appropriate THI model was predicted on the basis of correlation between THIs calculated from each model and physiological responses. The data of physiological parameters were linked to the THI calculated from identified THI model and threshold THI for each parameter was determined using segmented regression analysis (SegReg Software). The THI models; THI1{(1.8 × Tdb+32)-[(0.55-0.0055 × RH) × (1.8 × Tdb-26.8)]} and THI8{(0.8 × Tdb)+[(RH/100) × (Tdb-14.4)]+46.4)} were found to be equally appropriate for assessing environmental heat stress. Threshold THIs with respect to PR, RR and RT in Jamunapari goat were 71.78, 75.14 and 85.94, respectively and in Barbari goats, threshold THIs for PR and RR were 79.48 and 84.40, respectively. A threshold THI could not be identified for RT in Barbari goats. It can be concluded that THI1 and THI8 were the appropriate THI models for measuring environmental heat stress in goats. Results suggested that PR is the first physiological parameter which alters after the onset of heat stress and is followed by changes in RR and RT. On the basis of differential threshold THIs, it can be concluded that Barbari is better adapted than Jamunapari goats in semi-arid regions of India.

The gut microbiota is associated with psychiatric symptom severity and treatment outcome among individuals with serious mental illness.

BACKGROUND: Emerging evidence implicates the gut microbiota in central nervous system functioning via its effects on inflammation, the hypothalamic-pituitary axis, and/or neurotransmission. Our understanding of the cellular underpinnings of the brain-gut relationship is based almost exclusively on animal models with some small-scale human studies. This study examined the relationship between the gut microbiota and psychiatric symptom severity and treatment response among inpatients with serious mental illness. METHOD: We collected data from adult inpatients (N = 111). Measures of diagnoses, suicide severity, trauma, depression, and anxiety were collected shortly after admission, while self-collected fecal swabs were collected early in the course of hospitalization and processed using 16S rRNA gene sequencing and whole genome shotgun sequencing methods. RESULTS: Results indicate that depression and anxiety severity shortly after admission were negatively associated with bacterial richness and alpha diversity. Additional analyses revealed a number of bacterial taxa associated with depression and anxiety severity. Gut microbiota richness and alpha diversity early in the course of hospitalization was a significant predictor of depression remission at discharge. CONCLUSIONS: This study is among the first to demonstrate a gut microbiota relationship with symptom severity among psychiatric inpatients as well as a relationship to remission of depression post-treatment. These findings are consistent with animal models and limited human studies as well as with the broader literature implicating inflammation in the pathophysiology of depression. These findings offer the foundation for further studies of novel therapeutic approaches to the treatment, prevention of, or recurrence of serious mental illness.

Usability of daily SF36 questionnaires to capture the QALD variation experienced after vaccination with AS03A-adjuvanted monovalent influenza A (H5N1) vaccine in a safety and tolerability study.

BACKGROUND: This study aims to describe the short-term reactogenicity of the AS03-adjuvanted H5N1 vaccine expressed through adverse events (AEs) and quality-adjusted life-day (QALD) scores. The AEs are likely to be short-term and therefore the quality of life (QoL) questionnaire, SF-36v2, was administered daily to record changes over seven days. A more sensitive application of this instrument should allow for a better understanding of short-term tolerability of adjuvanted vaccines. METHODS: Participants (N = 50) received a 2-dose vaccination schedule. Solicited (collected daily: days 0 to 7 [post dose 1] and 21 to 28 [post dose 2]) and unsolicited (collected weekly until day 21) AEs were collected via diary cards. The QoL questionnaires were completed daily (days 0-6) and weekly (days 0, 6, 21, 27) after dose one. Questionnaire data were transformed into SF-6D scores to report QALDs. It was hypothesized post-hoc that the QALD and daily AEs scores should correlate if discrete QoL-changes were captured. RESULTS: Pain (92%) and muscle ache (66%) were the most commonly reported solicited local and general AEs respectively, neither increased in intensity nor in frequency after dose 2. No safety concerns were identified during the study. A correlation between the daily AEs and QALD scores existed (correlation coefficient, - 0.97 (p < 0.001)). The impact of the AEs scores on the QALD was marginal (- 0.02 max for one day). CONCLUSION: Similarly with other H5N1 studies, no safety concern was identified throughout the study. Some time-limited variations in QALD-scores were reported. Our results imply that daily administration of the SF-36v2 captures changes in QALD-scores. TRIAL REGISTRATION: ClinicalTrials.gov . NCT01788228. Registered 11 February 2013.

Insula and amygdala resting-state functional connectivity differentiate bipolar from unipolar depression.

OBJECTIVE: Distinguishing depressive episodes due to bipolar disorder (BD) or major depressive disorder (MDD) solely on clinical grounds is challenging. We aimed at comparing resting-state functional connectivity (rsFC) of regions subserving emotional regulation in similarly depressed BD and MDD. METHOD: We enrolled 76 in-patients (BD, n = 36; MDD, n = 40) and 40 healthy controls (HC). A seed-based approach was used to identify regions showing different rsFC with the insula and the amygdala. Insular and amygdalar parcellations were then performed along with diagnostic accuracy of the main findings. RESULTS: Lower rsFC between the left insula and the left mid-dorsolateral prefrontal cortex and between bilateral insula and right frontopolar prefrontal cortex (FPPFC) was observed in BD compared to MDD and HC. These results were driven by the dorsal anterior and posterior insula (PI). Lower rsFC between the right amygdala and the left anterior hippocampus was observed in MDD compared to BD and HC. These results were driven by the centromedial and laterobasal amygdala. Left PI/right FPPC rsFC showed 78% accuracy differentiating BD and MDD. CONCLUSION: rsFC of amygdala and insula distinguished between depressed BD and MDD. The observed differences suggest the possibility of differential pathophysiological mechanisms of emotional dysfunction in bipolar and unipolar depression.

Unusual Presentation of Necrotic Erythema Nodosum Leprosum on Scalp: A Case Report.

Lepra reactions are acute episodes occurring during the disease process of leprosy and are of 2 types: type 1 or reversal reaction and type 2 reaction or erythema odosumleprosum (ENL). In the episodes of lepra reaction several parts are affected including face and extremities like oral cavity. In the present case report we reported a rare case of lepromatous leprosy with necrotic ENL involving scalp apart from the usual sites. A 58 year old married male presented to us with complaints of spontaneous onset, recurrent eruption of multiple reddish raised painful lesions. Biopsy from the infiltrated skin over the back showed atrophic epidermis, free Grenz zone, diffuse and periadnexal macrophage granulomas with predominant mononuclear infiltrate, appandageal atrophy, fibrosis around the neural structures and leukocytoclastic vasculitis. Fites stain showed strong positivity for M. leprae. His routine blood investigations showed anemia (Hb = 7.8 gm%), neutrophil leukocytosis (TLC = 17,600, DLC = P66L28M4E2) and raised ESR (80 mm in the first hour). These bullous and necrotic lesions in leprosy may be a manifestation of severe type II reactions in patients with very high bacillary load.

Neurocognitive status is associated with all-cause mortality among psychiatric, high-risk liver transplant candidates and recipients.

OBJECTIVE: Judicious selection of potential liver transplant candidates and close monitoring of progress are essential to successful outcomes. Pretransplant psychosocial evaluations are the norm, but the relationship between psychosocial (and neurocognitive status) and longer term medical outcomes is understudied. This exploratory study sought to examine the relationship between objective measures of pretransplant psychosocial and neurocognitive status and service utilization, transplant status, and all-cause mortality. METHODS: This retrospective chart review examined outcomes among 108 psychiatric, high-risk liver transplant candidates up to four years following initial evaluation. Predictor variables of outcomes included demographic, medical, neurocognitive, psychological, and mental health treatment variables. RESULTS: Transplant status and neurocognitive functioning were independently associated with all-cause mortality. None of the other variables were associated with outcomes. CONCLUSIONS: Better neurocognitive functioning in high-risk liver transplant candidates may allow for greater involvement in medical care and/or compliance with treatment recommendations. More aggressive assessment and management of neurocognitive dysfunction may improve outcomes. Objective measures identified significant psychopathology typical of liver transplant candidates but were not associated with outcomes; engagement in specialized mental health care may have attenuated this relationship. Further study is needed to better understand the relationship between psychosocial functioning and outcomes.

Diverse models for anti-HIV activity of purine nucleoside analogs.

BACKGROUND: Purine nucleoside analogs (PNAs) constitute an important group of cytotoxic drugs for the treatment of neoplastic and autoimmune diseases. In the present study, classification models have been developed for the prediction of the anti-HIV activity of purine nucleoside analogs. RESULTS: The topochemical version of superaugmented pendentic index-4 has been proposed and successfully utilized for the development of models. A total of 60 2D and 3D molecular descriptors (MDs) of diverse nature were selected for building the classification models using decision tree (DT), random forest (RF), support vector machine (SVM), and moving average analysis (MAA). The values of most of these descriptors for each of the analogs in the dataset were computed using the Dragon software (version 5.3). An in-house computer program was also employed to calculate additional MDs which were not included in the Dragon software. DT, RF, and SVM correctly classified the analogs into actives and inactives with an accuracy of 89 %, 83 %, and 78 %, respectively. MAA-based models predicted the anti-HIV activity of purine nucleoside analogs with a non-error rate up to 98 %. Therapeutic active spans of the suggested MAA-based models not only showed more potency but also exhibited enhanced safety as revealed by comparatively high values of selectivity index (SI). The statistical importance of the developed models was appraised via intercorrelation analysis, specificity, sensitivity, non-error rate, and Matthews correlation coefficient. CONCLUSIONS: High predictability of the proposed models clearly indicates an immense potential for developing lead molecules for potent but safe anti-HIV purine nucleoside analogs.

Role of moving average analysis for development of multi-target (Q)SAR models.

In modern drug discovery era, multi target- quantitative structure activity relationship [mt- (Q)SAR] approaches have emerged as novel and powerful alternatives in the field of in-silico drug design so as to facilitate the discovery of new chemical entities with multiple biological activities. Amongst various machine learning approaches, moving average analysis (MAA) has frequently exhibited high accuracy of prediction of diverse biological activities against different biological targets and experimental conditions. Role of MAA in developing (Q)SAR models for prediction of single/dual or multi target activity has been briefly reviewed in the present article. Subsequently, MAA was successfully utilized for developing mt-(Q)SAR models for simultaneous prediction of anti-Plasmodium falciparum and anti-Trypanosoma brucei rhodesiense activities of benzyl phenyl ether derivatives. The statistical significance of models was assessed through intercorrelation analysis, sensitivity, specificity and Matthew's correlation coefficient. Proposed MAA based models were also validated using test set. High predictability of the order of 80% to 95% amalgamated with safety (indicated by high value of selectivity index) of proposed mt-(Q)SAR models justifies use of MAA in developing models in order to obtain more realistic and accurate results for prediction of anti-protozal activity against multiple targets. Active ranges of the proposed models can play a significant role in the development of novel, potent, versatile and safe anti-protozoal drugs with improved profile in terms of both anti-Plasmodium falciparum and anti-Trypanosoma brucei rhodesiense activities.

Detour matrix-based adjacent path eccentric distance sum indices for QSAR/QSPR. Part I: development and evaluation.

In the present study, three detour matrix-based topological indices (TIs) termed as adjacent path eccentric distance sum indices 1-3 (denoted by (A)ξ(1)(PDS), (A)ξ(2)(PDS) and (A)ξ(3)(PDS)) as well as their topochemical versions (denoted by (A)ξ(1c)(PDS), (A)ξ(2c)(PDS) and (A)ξ(3c)(PDS)) have been conceptualised. Values of the proposed TIs were computed for all possible cyclic and acyclic structures containing three, four, five vertices using an in-house computer programme. Proposed TIs were evaluated for discriminating power, degeneracy, intercorrelation and sensitivity towards branching as well relative position of substituent(s) in cyclic structures. Mathematical properties of one of the proposed TIs were also studied. Exceptionally high discriminating power, high sensitivity towards branching as well as relative position(s) of substituent(s) in cyclic structures and negligible degeneracy offer proposed indices a vast potential for use in characterisation of structures, similarity/dissimilarity studies, lead identification and optimisation, combinatorial library design and quantitative structure-activity/property/toxicity/pharmacokinetic relationship studies so as to facilitate drug design.

Detour matrix-based adjacent path eccentric distance sum indices for (Q)SAR/QSPR. Part II: application in development of models for COX-2 inhibitory activity of indomethacin derivatives.

In present study, adjacent path eccentric distance sum indices proposed in Part-I of the manuscript were successfully utilised for the development of models for cycloxygenase-2 (COX-2) inhibitory activity. Values of diverse molecular descriptors (MDs) for each of 38 indomethacin analogues involved in the dataset were computed. A total of 55 diverse MDs were ultimately shortlisted for further analysis. The suitable models were developed using decision tree (DT), random forest (RF) and moving average analysis (MAA). The DT identified the proposed topological index (TI)-(A)ξ(3)(PDS) as one of the important indices. The accuracy of prediction of DT, RF and MAA-based models varied from 81.58% to 97.37%. The statistical significance of proposed models was assessed through inter-correlation analysis, sensitivity, specificity, non-error rate and Mathews correlation coefficient. Proposed models offer vast potential for providing lead structures for the development of potent anti-inflammatory agents devoid of COX-1 side effects.

Fourth generation detour matrix-based topological descriptors for QSAR/QSPR - Part-2: application in development of models for prediction of biological activity.

Augmented path eccentric connectivity topochemical indices (reported in part-1 of the manuscript) along with 42 diverse non-correlating molecular descriptors (shortlisted from a large pool of 2D and 3D MDs) were successfully utilised for the development of models through decision tree, random forest and moving average analysis for the prediction of antitubercular activity of aza and diazabiphenyl analogues of active compound (6S)-2-Nitro-{[4-(trifluoromethoxy)benzyl]oxy}-6,7-dihydro-5H-imidazo[2,1-b][1,3] oxazine (PA-824). The statistical significance of the proposed models was assessed through overall accuracy of prediction, intercorrelation analysis, sensitivity, specificity and Matthew's correlation coefficient (MCC). The accuracy of prediction of the proposed models varied from a minimum of 81% to a maximum of ∼99%. High accuracy of prediction amalgamated with high MCC values clearly indicates robustness of the proposed models. The said models offer a vast potential for providing lead structures for the development of potent antitubercular drugs.

Models for H3 receptor antagonist activity of sulfonylurea derivatives.

The histamine H3 receptor has been perceived as an auspicious target for the treatment of various central and peripheral nervous system diseases. In present study, a wide variety of 60 2D and 3D molecular descriptors (MDs) were successfully utilized for the development of models for the prediction of antagonist activity of sulfonylurea derivatives for histamine H3 receptors. Models were developed through decision tree (DT), random forest (RF) and moving average analysis (MAA). Dragon software version 6.0.28 was employed for calculation of values of diverse MDs of each analogue involved in the data set. The DT classified and correctly predicted the input data with an impressive non-error rate of 94% in the training set and 82.5% during cross validation. RF correctly classified the analogues into active and inactive with a non-error rate of 79.3%. The MAA based models predicted the antagonist histamine H3 receptor activity with non-error rate up to 90%. Active ranges of the proposed MAA based models not only exhibited high potency but also showed improved safety as indicated by relatively high values of selectivity index. The statistical significance of the models was assessed through sensitivity, specificity, non-error rate, Matthew's correlation coefficient and intercorrelation analysis. Proposed models offer vast potential for providing lead structures for development of potent but safe H3 receptor antagonist sulfonylurea derivatives.

Models for the prediction of melanocortin-4 receptor agonist activity of 4-substituted piperidin-4-ol.

In the present study both classification and correlation techniques have been successfully employed for the development of the models of diverse nature for the prediction of melanocortin 4-receptor (MC4 R) agonist activity using a dataset comprising of 56 analogues of 4-substituted piperidine-4-ol derivatives. Decision tree (DT), random forest (RF), moving average analysis (MAA) and multiple linear regression (MLR) were utilised for development of the said models. The statistical significance of models was assessed through specificity, sensitivity, overall accuracy, Mathew's correlation coefficient (MCC) and intercorrelation analysis. High accuracy of prediction up to 98% was observed using these models. Proposed models offer vast potential for providing lead structures for the development of potent therapeutic agents for the treatment of male sexual dysfunction.

Classification models for safe drug molecules.

Frequent failure of drug candidates during development stages remains the major deterrent for an early introduction of new drug molecules. The drug toxicity is the major cause of expensive late-stage development failures. An early identification/optimization of the most favorable molecule will naturally save considerable cost, time, human efforts and minimize animal sacrifice. (Quantitative) Structure Activity Relationships [(Q)SARs] represent statistically derived predictive models correlating biological activity (including desirable therapeutic effect and undesirable side effects) of chemicals (drugs/toxicants/environmental pollutants) with molecular descriptors and/or properties. (Q)SAR models which categorize the available data into two or more groups/classes are known as classification models. Numerous techniques of diverse nature are being presently employed for development of classification models. Though there is an increasing use of classification models for prediction of either biological activity or toxicity, the future trend will naturally be towards the development of classification models capable of simultaneous prediction of biological activity, toxicity, and pharmacokinetic parameters so as to accelerate development of bioavailable safe drug molecules.

Fourth generation detour matrix-based topological indices for QSAR/QSPR - part-1: development and evaluation.

In the present study, four detour matrix-based Topological Indices (TIs) termed as augmented path eccentric connectivity indices 1-4 (denoted by (AP)ξ(1)(C), (AP)ξ(2)(C), (AP)ξ(3)(C) and (AP)ξ(4)(C)) as well as their topochemical versions (denoted by (AP)ξ(1c)(C), (AP)ξ(2c)(C), (AP)ξ(3c)(C) and (AP)ξ(4c)(C)) have been conceptualised. A modified detour matrix termed as chemical detour matrix (Δ(c)) has also been proposed so as to facilitate computation of index values of topochemical versions of the said TIs. Values of the proposed TIs were computed for all the possible structures containing three, four and five vertices using an in-house computer program. The said TIs exhibited exceptionally high discriminating power and high sensitivity towards branching/relative position of substituent(s) in cyclic structures amalgamated with negligible degeneracy. Due care was taken during the development of TIs so as to ensure that reduction in index values of complex chemical structures to be within reasonable limits without compromising discriminating power. The mathematical properties of one of the proposed TIs have also been studied. With exceptionally high discriminating power, high sensitivity towards branching as well as relative position(s) of substituents in cyclic structures and negligible degeneracy, the proposed indices offer a vast potential for use in characterisation of structures, similarity/dissimilarity studies, lead identification and optimisation, combinatorial library design and quantitative structure-activity/property/toxicity/pharmacokinetic relationship studies.

Prediction of pharmacokinetic parameters.

In silico tools specifically developed for prediction of pharmacokinetic parameters are of particular interest to pharmaceutical industry because of the high potential of discarding inappropriate molecules during an early stage of drug development itself with consequent saving of vital resources and valuable time. The ultimate goal of the in silico models of absorption, distribution, metabolism, and excretion (ADME) properties is the accurate prediction of the in vivo pharmacokinetics of a potential drug molecule in man, whilst it exists only as a virtual structure. Various types of in silico models developed for successful prediction of the ADME parameters like oral absorption, bioavailability, plasma protein binding, tissue distribution, clearance, half-life, etc. have been briefly described in this chapter.

Predicting biological activity: computational approach using novel distance based molecular descriptors.

Four novel distance based molecular descriptors termed as superpendentic eccentric distance sum indices 1-4 (denoted by:∫P-1EDS, ∫P-2EDS, ∫P-3EDS and ∫P-4EDS) as well as their topochemical counterparts (denoted by:∫cP-1EDS, ∫cP-2EDS, ∫cP-3EDS and ∫cP-4EDS) have been conceptualized and developed in the present study. The sensitivity towards branching, discriminating power, and degeneracy of the proposed novel descriptors were investigated. Utility of these indices was investigated for development of models through decision tree and moving average analysis for the prediction of human corticotropin releasing factor-1 receptor binding affinity of substituted pyrazines. A wide variety of 46 2D and 3D molecular descriptors including proposed indices was employed for development of models through decision tree and moving average analysis. The calculation of most of these descriptors for each compound of the dataset was performed using online E-Dragon software (version 1.0). An in-house computer programme was also employed to calculate additional topological descriptors which did not figure in E-Dragon software. The decision tree classified and correctly predicted the input data with an impressive accuracy of 92% in the training set and 71% during cross-validation. A total of three descriptors, identified by decision tree, were subsequently utilized for development of suitable models using moving average analysis. These models predicted human corticotropin releasing factor-1 receptor binding affinity with an accuracy of ≥85%. The statistical significance of models was assessed through sensitivity, specificity and Matthew's correlation coefficient. High discriminating power, high sensitivity towards branching amalgamated with negligible degeneracy offer proposed descriptors a vast potential for use in the quantitative structure-activity/property/toxicity relationships so as to facilitate drug design.