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Female genital tuberculosis (GTB) contributes significantly to infertility in low- and middle-income countries. Dissemination of infection from pulmonary and extrapulmonary sites is the major reason for causation of GTB. Additionally, sexual transmission of GTB from male partners has been reported. We selected 81 couples desiring babies from an in vitro fertilization clinic. We used multiplex-PCR for mycobacterial detection in semen of males, in the endometrium of their female counterparts and in the products of conception (POC) from miscarriage. Data interpretation shows that these pregnancies failed owing to sexual transmission of mycobacteria. We noticed by multiplex PCR that mycobacterial infestation in the female can take place in either endometrium or POC from asymptomatic males harbouring mycobacteria in their semen. Therefore, we propose sexual transfer of mycobacteria to be a probable cause of miscarriage. Thus, we suggest multiplex PCR based screening of semen for all males of the couples attempting successful childbirth.

The problem of sewage disposal has received great attention worldwide. The raw sewage contains a variety of high inorganic and organic matters that affect natural water environment. To mitigate such problem, sewage may be recycled through aquaculture practice. Sewage recycling in aquaculture enhances water productivity through nutrients input. Proper loading of sewage ensures viable aquaculture; otherwise, fish mortality occurs due to poor water quality. To optimize sewage application, two different experiments were conducted, each with four treatments. In both experiments, three fish species namely rohu (Labeo rohita Hamilton, 1822), mrigal (Cirrhinus mrigala Hamilton, 1822), and bata (Labeo bata Hamilton, 1822) were tested in triplicate in FRP (Fibre-Reinforced Plastic) tanks. Different sewage concentrations (0, 25%, 50% and 75%) used in first experiment were prepared by mixing freshwater, showing Biochemical Oxygen Demand (BOD) 2.0 ± 0.4 mg/L, 10.8 ± 1.4 mg/L, 19.6 ± 1.5 mg/L and 41.6 ± 2.58 mg/L, respectively. After 30 days rearing, results showed ≥75% fish survival in sewage concentrations up to 50% with BOD level 19.6 ± 1.5 mg/L. Less than 50% fish survived in 75% sewage concentration, with BOD level as 41.6 ± 2.58 mg/L. The second experiment was conducted for 90 days considering 50% sewage concentration as basal dose with BOD level as 19.6 mg/L as an acceptable limit for fish survival. Split doses of sewage were applied in T1, T2 and T3 treatments fortnightly, weekly and semi-weekly intervals, while single dose was used in C (control) treatment. Application of split doses resulted better hydro-biological changes, including nutrients recovery, in T1, T2 and T3 than that of single dose in control. Fish growth plotted with net primary productivity (NPP), phytoplankton and zooplankton densities exhibited positive correlations in T2 (12 doses) and T3 (24 doses), considered as optimal doses to ensure better water productivity for desirable fish production than sewage with single dose or limited doses (6 doses).

A transmission line fault classification scheme is proposed in this article using Poincare-based correlation analysis of three-phase fault currents. The method segments each fault signal into two equal time-split components and computes correlation coefficient between these two time-split signals. The fault current signals of the directly affected line(s) observe an abrupt monotonic rise, compared to the indirectly affected phases. This sudden rise in magnitude is expressed with correlation coefficients between the two almost consecutive time-split components of signal, time shifted by delay index. This method emphasizes this monotonic nature of increment of the fault current, enabling prompt fault detection. Further analysis of three phases of fault signals independently yields a set of correlation coefficients for ten different fault prototypes, which are used to develop fault classifier signatures for direct classification. The proposed method yields high classification accuracy of 99.76% using only (1/6)th of the post-fault noisy signal with fault resistance varying from 0.01 to 100Ω. Besides, analysis of only one end single discards the requirement of time synchronous signal acquisition from both ends. Finally, use of simple analysis reduces the computational burden compared to several contemporary methods.

Transmission line faults are most common in long distance power transmission system. Classification of faults is crucial in removal of the faulted line from supply end in order to discontinue the unwanted flow of power through the fault point. The proposed work illustrates a simple method of classification of faults in transmission line using Principal Component Analysis- (PCA) based approach. The proposed method uses to extract fault features in terms of Principal Component Index (PCI), followed by a threshold-based analysis of the PCI values. Development of two threshold values helps in segregating the three different levels of fault disturbance in terms of the PCI values, thereby, developing fault signatures for classification. A 150 km transmission line has been modeled in EMTP for simulating the different fault prototypes, followed by analysis in MATLAB environment. Fault locations and fault resistances are also varied in order to enhance robustness in the classifier. Most importantly, noise level of the fault signals is varied in wide practical range; especially, signal to noise ratio (SNR) is increased to 15 dB to examine the robustness of the proposed classifier under severe noisy condition, where the same is found to produce 99.78% classifier accuracy using half cycle post-fault line currents only; thus, establishing the insensitivity of PCA toward noise.

Recent studies suggest ex vivo modeling of neuronal injury is a robust approach for the mechanistic study of neurodegeneration. Melatonin, an indolamine, is a versatile molecule with antioxidative, antiapoptotic, neuroprotective, and anti-inflammatory properties. While melatonin has been studied as a therapeutic agent for spinal cord injury (SCI) related neuronal cell loss, its actions in organotypic slice cultures approximating SCI effects are less well understood. The actions of melatonin were therefore examined following exposure of cultured rat spinal cord slices to glutamate excitotoxicity. Exposure to glutamate (500 μM) for 4 hours induced neuronal degeneration that was prevented by 0.5 μM melatonin (applied immediately or 4 hours following glutamate exposure). Decreased internucleosomal DNA fragmentation, Bax:Bcl-2 and calpain:calpastatin ratios, caspase 8, 9 and 3 activities in slice cultures were measured following melatonin treatment. Melatonin receptor (MTR1, MTR2) mRNA levels were increased in the melatonin treated spinal cord slices. To confirm melatonin receptor-mediated protection, slice cultures were treated with 10 or 25 μM luzindole (melatonin receptor antagonist) at 0 and 4 hours, respectively, after glutamate exposure. Luzindole significantly decreased the ability of melatonin to prevent cell death in the sliced culture model. These results suggest melatonin receptors may provide a pathway for therapeutic applications to prevent penumbral neuron loss following SCI.

This paper investigates the application of Principal Component Analysis (PCA) for the development of a simple method of classification and localization of power system faults for a 150 km long transmission line. The proposed work uses only a quarter cycle pre-fault and half cycle post-fault receiving end line current signals for fast identification and isolation of the faulty line. This work analyzes current data of ten different fault classes. The fault signals are recorded at fourteen intermediate geometric locations, out of which, three locations are used for developing the PCA-ratio based classifier and a total of six locations are used for developing the localizer model. PCA is applied here to develop PC score indices, based on which, fault signature curve is developed using best fit analogy. This curve works as the key signature of localizer for each class and phase. The work is made more practically suited by incorporating noise in the signals. Thus an effort has been made in the proposed work for developing a complete practical fault diagnosis algorithm with an aim to achieve high level of accuracy both to classify and localize fault. The proposed classifier is found to produces 100% accuracy, and the localizer is found to achieve an average localization error of only 0.1189% for 40 dB SNR and 0.3965% error at a further higher noise level of 25 dB SNR, with less than 4% of maximum error.

Solar photovoltaic (PV) arrays connected with the microgrid system consist of multiple strings interconnected in different ways. This paper deals the diagnosis of faults that occurred in one or mul...

Electrical power transmission lines are most vulnerable to different faults due frequent atmospheric hazards. Hence, fault detection and classification are imperative to restrict unwanted power outage by isolation of the faulted line. A probabilistic neural network (PNN)-based fault classification methodology is proposed here. This work uses differencing-based modulated fault signals as input to the PNN architecture to extract fault features in terms of three-phase fault intensity index, which are further analyzed for direct classification of faults aided by a decision tree like study. Simulation of faults has been carried out in practical alike simulation environment with variation of fault location, fault resistance and inherent power line noise, which helps to develop robustness of fault analyzer. Classifier accuracy of 99.33% is achieved here, and more importantly, using only (1/6)th fraction of post fault signals, which is considerably low compared to similar contemporary works. Furthermore, the proposed classifier is equipped with the ability of detecting ground fault without inspecting the neutral current. This method uses only 35.7% of the total fault locations for training, and the rest for validating the model, which is also an above average performance. Finally, the analyzer comprises of simple PNN model as the only classifier, hence possess considerably low computational complexity.

This paper considers the duality between stochastic frontier production and cost functions, under the assumption of cost minimization (technical and allocative inefficiency) and dependence structure for both measures of technical and allocative inefficiency as assumed by (Schmidt and Lovell, Journal of Econometrics 13:83–100, 1980). However, the assumed dependence structure comprise of positive dependence for higher technical inefficiency and higher (positive) allocative inefficiency; and negative dependence for higher technical inefficiency and higher (negative) allocative inefficiency through a mixture of copula model. The dependence structure is presented by a multivariate Farlie–Gumbel–Morgenstern (FGM) copula as there will be choice of probability distributions for both technical and allocative inefficiency. The proposed model is estimated using simulated maximum likelihood (SML) method. An application of the illustrated model to the US electricity utility data (Greene, Journal of Econometrics 46:141–164, 1990) shows a significant dependence between technical and allocative inefficiency.

Wavelet transform is a widely used mathematical tool for analyzing fault signals. But the major problem lying with wavelet analysis is in the selection of the appropriate mother wavelet. Besides, selection of the optimal level of decomposition is also important since the complexity and time of computation increases proportionately as the level of decomposition increases. In this work, we have proposed a method for selecting an optimal mother wavelet and optimal level of decomposition of three phase fault current signals of induction motor, in order to achieve appreciably accurate result with optimized complexity. Here, we have analyzed the results using mother wavelets from the Daubechies (db), Coiflets (Coif) and Symlets (Sym) gropus and decomposed the fault current signals of three phase induction motors upto five levels. We have also varied the loading on the motor in three discrete steps in order to incorporate robustness to the model and analyzed the four major quality factors, viz., signal to noise ratio (SNR), root Mean square error (RMSE), peak signal to noise ratio (PSNR) and correlation coefficient (CC) for reaching a conclusion regarding optimum mother wavelet selection in favour of Symlet5 mother wavelet decomposed at level 4.

Prediction of unknown fault of induction motor is an important task to prevent it from unscheduled shutdown. Here, an unknown fault of induction motor has been classified and authenticated from other type of faults using multiclass support vector machine (SVM) following decision-directed acyclic graph (DDAG). Three phase current data samples are collected from induction motors with different known type of fault conditions and one induction motor with unknown type fault condition. Experiment has been performed for motor fault current signature analysis (MFCSA) to identify the type of unknown fault among the mixture of various type of faults. A feature extraction and dimensions reduction process called principal component analysis (PCA) is used to extract the information from fault current signature of each faulty motor and two eigenvalues of stator currents which are called principal components are effective fault features of the motors are captured with the help of PCA transformation. One vs one (OVO) SVM algorithm is applied to separate each pair of classes out of six classes nonlinearly by RBF type kernel assigning the unknown test sample to the class. The multiple PC values of each phase of each faulty induction motor are considered as one class. The OVO-SVM constructs n (n-1)/2 no of classifiers for n class problem of each phase and DDAG technique is used to create a directed acyclic graph using the classifiers to take accurate decision about the classification of the unknown fault. The unknown fault is classified for each phase among different type of faults depending on maximum membership count generated by classifiers and the fault is also authenticated from the results of DDAG of three phases.

Transmission line fault classification is one of the most studied themes of power system analysis and research. This is of utmost importance to isolate the faulted phase for avoiding undue drainage of bulk power during fault. This paper presents a simple and effective method for classification of power system faults in a transmission line using a multivariate statistical method like Principal Component Analysis (PCA). Half-cycle post-fault sending end fault transient current signals are used as the working data for the work, which are normalized, scaled, filtered and finally differentiated. Differentiation of the fault currents is a key method used here in order to highlight particularly the post-fault transient oscillations compared to the original fault transients. These increased oscillations of the modified signals are analyzed using PCA, which extracts fault features in terms of Principal Component scores, which, in turn, are remodeled to develop Principal Component Indices (PCIs). These PCI values as obtained from the analysis of differentiated signals are found higher compared to un-modulated signals in most of the cases; thus yielding more prominent features to distinguish different fault classes, as well as fault and no-fault conditions. The algorithm is tested by varying fault locations at an interval of 10 km. Besides, the proposed scheme is made more practical by incorporating power system noise, as well as varying fault inception angle at intervals of 45°, finally to yield an overall classifier accuracy of 99.41%. This, in turn, validates the robustness of the proposed model.

Line to Ground (LG) and Line to Line (LL) faults are the two most frequently encountered faults in any power system network. For the purpose of designing advanced protection systems, detection of the location as well as the identification of the type of fault, from a remote location is of paramount importance. In this paper a Discrete Wavelet Transform based statistical analysis has been carried out to detect the fault type and location of LG and LL faults. IEEE standard 9 bus system has been considered for this purpose. Faults are made to occur in the load buses and outgoing currents from the generator buses are analyzed by Discrete Wavelet Transform (DWT) as these current waveforms are non-stationary in nature. Statistical parameters are calculated from the approximate and detail coefficients which have been derived from the DWT. Based upon these parameters, a rule set has also been made. Simulation work is performed with the help of MATLAB. Methods proposed here can be helpful for designing better protection schemes.

The paper presents a principal component analysis (PCA)-based method for localization of various power system faults in a 150 km long single side fed transmission line using quarter-cycle pre-fault and half-cycle post-fault sending end line current signals. The proposed work uses fault signals of ten different types of seven intermediate locations along the length of the line to develop three-phase PCA score indices. The localizer model is also designed for practical fitment, with fault signals contaminated with power system noise. These seven sets of indices are further used with the best-fit curve fitting method in the MATLAB environment to develop fault curves. Minimum root mean square error criteria are followed for selecting the fit type. Each fault class is designed with the required number of curves to estimate fault location. The proposed work produces a highly accurate localization, with only 0.1271% average percentage error for fault localization, and a maximum percentage error of 0.5821% for the 150 km line.

The earlier methods adopted for stability analysis of synchronous machines were based on approximations and idealizing assumptions. As such, the accuracy of prediction was limited. The advent of fast-acting computers having large memory space opened up the avenue for accurate analysis by handling detailed modelling. The earlier methods were based on equal area criterion or on swing equation or some of its modified form. But, the methods applied nowadays uses state space representation of the dynamic equations. The paper shows the steps to develop state space models (both with current and flux-linkage states) for transient stability analysis and the methods of linearizing the model about the operating point to assess dynamic stability. Two case studies have been given at the end to illustrate the methods.

Analysis of asynchronous conditions in a synchronous machine was initially made by classical method following the generalized machine theory. Later on, it took a turn after the advent of solid iron rotor/solid iron pole-shoe. It was noted that there is a discrepancy between computed and actual quantities between results obtained by the classical and the non-classical model. It was gradually understood that the reason behind this discrepancy was the effect of distributed eddy currents in the solid metallic parts. Authors like B.J. Chalmer, B. Adkins, A. Canay, and others, contributed much to the analysis of asynchronous conditions by adopting new techniques. The paper presents the methodology for using these techniques to estimate the asynchronous variables under loss of excitation (LOE), also to find the same by classical method based on generalized machine theory.

Transient stability of an alternator under fault condition and after removal of the fault has been studied in great details by many authors. The stability studies have been made either on grid systems or on one machine on infinite bus idealization. The saliency effect, saturation and damping (due to both damper winding and solid iron rotor) have been accounted for by many authors. But in many cases, the effect of fast-acting control systems like automatic voltage regulator and exciter has not been considered. Transients caused by the turbine governor are slow and may be neglected, but not the fast-acting exciter control. This paper is devoted to assessment of stability in the presence of AVR exciter. A suitable model of IEEE has been chosen for the specific case study, which has been subsequently mapped to state space. The composite model has been developed from which both transient stability and dynamic stability have been assessed. The boiler and firing control is extremely sluggish—hence their effect has been neglected.

Purpose This study aims to assess the biodiversity of the study area and estimate the carbon stock of two dry deciduous forest ranges of Banka Forest Division, Bihar, India. Design/methodology/approach The phytosociological analysis was performed and C stock estimation based on volume determination through nondestructive methods was done. Findings Phytosociological analysis found total 18,888 [14,893 < 10 cm (diameter at breast height) dbh] and 2,855 (1,783 < 10 cm dbh) individuals at Banka and Bounsi range with basal area of 181,035.00 cm2 and 32,743.76 cm2, respectively. Importance value index was highest for Shorea robusta in both the ranges. Species diversity index and dominance index, 1.89 and 1.017 at Banka and 1.99 and 5.600 at Bounsi indicated the prevalence of biotic pressure. Decreased dbh and tree height resulted in a lowered growing stock volume as 59,140.40 cm3 ha−1 (Banka) and 71,306.37 cm3 ha−1 (Bounsi). Total C stock at Banka and Bounsi range was 51.8 t ha-1 and 12.56 t ha−1, respectively where the highest C stock is recorded for Shorea robusta in both the ranges (9.8 t ha−1 and 2.54 t ha-1, respectively). A positive correlation between volume, total biomass and basal area of tree species with C stock was observed. R2 value for Banka range was 0.9269 (volume-C stock), 1 (total biomass-C stock) and 0.647 (basal area-C stock). Strong positive correlation was also established at Bounsi range with R2 value of 1. Considering the total forest area enumerated, C sequestration potential was about 194.25 t CO2 (Banka) and 45.9 t CO2 (Bounsi). The valuation of C stock was therefore US$2,525.25 (Banka) and US$596.70 (Bounsi). Practical implications The research found the potentiality of the study area to sequester carbon. However, for future, the degraded areas would require intervention of management strategies for restoration of degraded lands and protection of planted trees to increase the carbon sequestration potential of the area. Originality/value Present study is the first attempt to assess the phytosociology and estimate the regulatory services of forest with respect to biomass and carbon stock estimation for the Banka forest division of Bihar.

Allometric regression models are one of the common methods of carbon stock estimation based on growing stock data conversion to estimates of above ground biomass (AGB). Therefore, allometric model selection is important functional aspect that has considerable influence on accuracy of biomass estimation. As destructive sampling is restricted in our study area, the site specific biomass model is developed for the first time based upon the forest inventory data that includes measurements of diameter at breast height (DBH) and tree height (H). To minimize the error in AGB estimation, intensive sampling was done where 78,201 individual tree were enumerated (6034 quadrats laid over 1207 plots). 20 locally abundant tree species were assessed. Tree volume and biomass were calculated and examined for best fit allometric model for the area. Species-specific models were established which best fits with the DBH as predictor variable. For multi-species models, inclusion of wood density (WD) enhanced the model fitness with increased adjusted R2 by 99.9%. Significant variations in predicted and observed values were noticed while considering the regional and pan-tropical models (model prediction error − 614.364 to 288.304%). Therefore, development of local models would provide more accurate AGB estimates. Best fit multi-species allometric model in our study is represented by ln (AGB) = a + b ln (DBH) + c ln (H) + d ln(WD). The equation developed for tropical forest of Eastern India applicable for Sal zone of Bihar is ln(AGB) = − 0.886 + 2ln(DBH) + ln(H) + ln(WD).

Glioblastoma (GB) remains an incurable and deadly brain malignancy that often proves resistant to upfront treatment with temozolomide. Nevertheless, temozolomide remains the most commonly prescribed FDA-approved chemotherapy for GB. The DNA repair protein methylguanine-DNA methyl transferase (MGMT) confers resistance to temozolomide. Unsurprisingly temozolomide-resistant tumors tend to possess elevated MGMT protein levels or lack inhibitory MGMT promotor methylation. In this study, cultured human temozolomide resistance GB (43RG) cells were introduced to the MGMT inhibitor O6-benzylguanine combined with temozolomide and either LY2835219 (CDK 4/6 inhibitor) or LY2157299 (TGF-βRI inhibitor) seeking to overcome GB treatment resistance. Treatment effects were assessed using MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay, western blot, cell viability, and cell cycle progression. Our in vitro study demonstrated that sequential treatment of O6-Benzylguanine with either LY2385219 or LY2157299-enhanced temozolomide enhanced sensitivity in MGMT+ 43RG cells. Importantly, normal human neurons and astrocytes remained impervious to the drug therapies under these conditions. Furthermore, LY2835219 has additional anti-proliferative effects on cell cycling, including induction of an RB-associated G (1) arrest via suppression of cyclin D-CDK4/6-Rb pathway. LY2157299 enhances anti-tumor effect by disrupting TGF-β–dependent HIF-1α signaling and by activating both Smad and PI3K-AKT pathways towards transcription of S/G2 checkpoints. This study establishes the groundwork for the development of a combinatorial pharmacologic approach by using either LY2385219 or LY2157299 inhibitor plus O6-Benzylguanine to augment temozolomide response in temozolomide-resistant GB cells.

The proposed work illustrates a simple research approach to identify the type of fault in a three-phase overhead single-end-fed long transmission line. Multivariate statistical methods like principal component analysis (PCA) alone, and in combination with probabilistic neural network (PNN), have been applied here to classify fault. An attempt has been made to use the PCA features obtained from the analysis of electrical parameters for each of the faults, in two ways. The first approach of fault classification is based on analyzing the PCA features by a modified ratio-based analysis. In the second method, an attempt has been made to use the PCA features directly to a structured PNN model. Electromagnetic Transient Program simulation software has been used to simulate a transmission line model. Sending-end three-phase line currents corresponding to various faults carried out at different geometric distances along the transmission line have been analyzed in MATLAB environment. The proposed algorithms are tested with unknown and intermediate distant faults with variable fault resistance to validate the same. Finally, a comparative analysis of the proposed two methods is illustrated, which shows 100% classifier accuracy of both the models.

Low currents and voltage levels in a microgrid system make the fault detection by the conventional method, very difficult. Due to ground faults voltage level drops to a very low level and thus, it ...

Background: Meningiomas represent ∼30% of primary central nervous system (CNS) tumors. Although advances in surgery and radiotherapy have significantly improved survival, there remains an important...

There are a number of problems in electrical machines which do not have any closed-form solution. They are to be solved by numerical analysis. Such problems are found in the analysis of synchronous and induction machines, also in field-theoretic analysis. One such problem is found in the analysis of salient pole hydro-generator. The paper deals with numerical solution of the problem. The problem concerns power generated, power angle and performance variables. The problem has been solved by a number of methods. Out of various methods, the Newton–Raphson method has been recommended as it gives the fastest solution. There is no closed-form solution also for saturation in an alternator if a fitting equation is not used. This problem has also been treated.

Allelic losses of the q13.3 region of chromosome 19 have been documented in all major types of diffuse gliomas, strongly suggesting the presence of a 19q13.3 tumor suppressor gene responsible for these malignancies. The P78 gene precisely maps to 19q13.3, the glioma candidate region, and encodes a recently identified novel protein (P78). The purpose of this study was to determine P78 protein expression in gliomas. Serial analysis of gene expression (SAGE) reveals P78 mRNA expression to be significantly reduced in high-grade gliomas such as glioblastoma (GB), as compared with the low-grade tumors including astrocytomas, oligodendrogliomas, and ependymomas. We observed the distribution of staining of P78 protein was concentrated on the cell membranes of the luminal epithelial cells, not cytoplasm. In contrast, the pre-immune serum controls demonstrated no staining. These results demonstrate that P78 protein is highly expressed in the cytoplasmic membranes of low but not high-grade astrocytomas, and correlates with grade of malignancy. In these double immunostaining experiments, the anti-Map-2 and anti-NeuN antibodies did not stain round cells that were stained with the anti-P78 carboxyl-terminal peptide antibodies, demonstrating that these round cells were not neurons, and likely protoplasmic astrocytes. Current results also suggest that the astrocytes stained with the anti-P78 carboxyl-terminal peptide antibody are likely protoplasmic astrocytes. We also observed preincubation of anti-P78 carboxyl-terminal antibodies with immunizing peptides abolished immunostaining in gliomas. These results suggest a role for the P78 protein in the process of abnormal growth in glial tumors.

Polyploid giant cancer cells (PGCC) represent a poorly understood, small subpopulation of tumor cells that are increasingly being recognized for their critical role in therapy resistance, metastasis, and cancer recurrence. PGCC have the potential to generate progeny through primitive or cleavage‐like division, which allows them to evade antimitotic insults. We recently demonstrated that the sphingolipid enzyme acid ceramidase (ASAH1) is required for this process. Since specific ASAH1 inhibitors are not clinically available, we investigated whether tamoxifen, which interferes with ASAH1 function via off‐target effects, has a potential clinical benefit independent of estrogen signaling. Our results show that tamoxifen inhibits generation of PGCC offspring in prostate cancer, glioblastoma, and melanoma cells. Analysis of two state‐level cancer registries revealed that tamoxifen improves survival outcomes for second, nonbreast cancers that develop in women with early stage breast cancer. Our results suggest that tamoxifen may have a clinical benefit in a variety of cancers that is independent of estrogen signaling and could be due to its inhibition of acid ceramidase. Thus the distinct application of tamoxifen as potentially a first‐in‐class therapeutic that inhibits the generation of PGCC offspring should be considered in future clinical trials., This study suggests that tamoxifen has an estrogen‐signaling independent clinical benefit in a variety of cancers that could be due to acid ceramidase inhibition. This would make tamoxifen a first‐in‐class therapeutic against formation of PGCC progeny.

Chronic obstructive pulmonary disease has got significant cardiovascular morbidity and mortality. The adverse cardiac effect can be picked up early by Electrocardiography (ECG) and echocardiography. Here, in this study, we tried to find out the different ECG changes in COPD and its correlation with disease duration, severity and other factors. We conducted a cross-sectional observational study in the Assam Medical College, Dibrugarh for a span of one year where 234 spirometrically confirmed COPD patients had undergone ECG. Most of the patients belonged to GOLD stage III (40%) and P-pulmonale was the most common ECG abnormality (63.3%). Other ECG findings were right axis deviation, right ventricular hypertrophy, incomplete right bundle branch block, S1Q3T3, S1Q3 pattern and Atrial Fibrillation (AF), having an increasing trend of abnormalities with the severity of GOLD stage. Increased incidence of AF is due to severity and longer duration of the disease. AF and right axis deviation occur more in smokers. Low voltage ECG is a nonspecific finding. The ECG changes were well correlated with disease severity and duration. How to cite this article:Pal A, Das A, Thakuria J. Electrocardiographic Changes in Chronic Obstructive Pulmonary Disease - An observational study from North-East of India. J Commun Dis 2020; 52(1): 32-37. DOI: https://doi.org/10.24321/0019.5138.202005

Teachers attitude towards teaching facilities and feasibilities and work environment play an important role in elementary education in this era where parents have enough choice for their children. This study was conducted in rural areas of West Bengal on teachers attitude on teaching facilities and work environment of private and public schools. The study found award for good deed, extra administrative works, strict regulations against sexual harassment and bullying, and participation in conducting co-curricular activities are related with gender of teachers. Also, healthy work environment, co-operation from non-teaching staff, extra administrative works, lack of technical facility for advanced teaching, regular skill enhancement for teachers, were given preference by the teachers who were trained. Moreover, having white board, regular skill enhancement for teachers, and award for good deed were preferred by the teachers with private school. &nbsp

Spinal cord injury (SCI) causes significant mortality and morbidity. Currently, no FDA-approved pharmacotherapy is available for treating SCI. Previously, low doses of estrogen (17β-estradiol, E2) were shown to improve the post-injury outcome in a rat SCI model. However, the range of associated side effects makes advocating its therapeutic use difficult. Therefore, this study aimed at investigating the therapeutic efficacy of Premarin (PRM) in SCI. PRM is an FDA-approved E2 (10%) formulation, which is used for hormone replacement therapy with minimal risk of serious side effects. The effects of PRM on SCI were examined by magnetic resonance imaging, immunofluorescent staining, and western blot analysis in a rat model. SCI animals treated with vehicle alone, PRM, E2 receptor antagonist (ICI), or PRM + ICI were graded in a blinded way for locomotor function by using the Basso–Beattie–Bresnahan (BBB) locomotor scale. PRM treatment for 7 days decreased post-SCI lesion volume and attenuated neuronal cell death, inflammation, and axonal damage. PRM also altered the balance of pro- and anti-apoptotic proteins in favor of cell survival and improved angiogenesis and microvascular growth. Increased expression of estrogen receptors (ERs) ERα and ERβ following PRM treatment and their inhibition by ER inhibitor indicated that the neuroprotection associated with PRM treatment might be E2-receptor mediated. The attenuation of glial activation with decreased inflammation and cell death, and increased angiogenesis by PRM led to improved functional outcome as determined by the BBB locomotor scale. These results suggest that PRM treatment has significant therapeutic implications for the improvement of post-SCI outcome.

Few cases have been reported of the diagnosis and treatment of glioblastoma (GB) during pregnancy. Subsequently, surgical, medical, and obstetrical management of complicated primary central nervous system malignancy in antepartum and postpartum patients remains under-investigated. The authors report the case of a 24-year-old female patient who developed generalized tonic-clonic seizures and focal neurologic deficits. MRI imaging (3T Skyra, Siemens, Erlangen, Germany) revealed an intracranial mass suspicious for malignant tumor and surgical resection under awake sedation was scheduled. The patient was incidentally found to be in her first trimester of pregnancy. Using neuronavigation, neurophysiologic monitoring, and conscious sedation the tumor was debulked successfully and histopathologic analysis confirmed giant cell glioblastoma, WHO Grade IV, 1p/19q intact, IDH wild-type, with NF1 p.Y2285fs and RB1 p.S318fs somatic mutations. Post-surgical oncologic management continued with fractioned radiotherapy and use of the Optune® device. The patient underwent uncomplicated cesarean section at 34-weeks gestation, the child remains healthy and the patient remains disease-disease free at 1-year. Thus, this case presents an approach to management of complicated GBM during first trimester pregnancy.

Glioblastoma (GB) is an aggressive tumor showing extensive intertumoral and intratumoral heterogeneity. Several possible reasons contribute to the historical inability to develop effective therapeutic strategies for treatment of GB. One such challenge is the inability to consistently procure high-quality biologically preserved specimens for use in molecular research and patient-derived xenograft model development. No scientifically derived standardized method exists for intraoperative tissue collection specifically designed with the fragility of RNA in mind.In this investigation, we set out to characterize matched specimens from 6 GB patients comparing the traditional handling and collection processes of intraoperative tissue used in most neurosurgical operating rooms versus an automated resection, collection, and biological preservation system (APS) which captures, preserves, and biologically maintains tissue in a prescribed and controlled microenvironment. Matched specimens were processed in parallel at various time points and temperatures, evaluating viability, RNA and protein concentrations, and isolation of GB cell lines.We found that APS-derived GB slices stored in an APS modified medium remained viable and maintained high-quality RNA and protein concentration for up to 24 hours.Our results showed that primary GB cell cultures derived in this manner had improved growth over widely used collection and preservation methods. By implementing an automated intraoperative system, we also eliminated inconsistencies in methodology of tissue collection, handling and biological preservation, establishing a repeatable and standardized practice that does not require additional staff or a laboratory technician to manage it.

Paul, B.N., Das, A., Mandal, R.N., Singh, P., Adhikari, S., Ghosh, K., Chowdhury, D., Chakrabarti, P.P. and Giri, S.S. 2020. Protein requirement of Ompok bimaculatus (Bloch, 1794) larvae. Animal Nutrition and Feed Technology, 20: 525–533.