In vitro regeneration through adventitious buds in Wattakaka volubilis, a rare medicinal plant

High frequency shoot regeneration from in vitro derived leaf explants of Wattakaka volubilis (L.f.) Stapf was achieved through callus mediated organogenesis. Organogenic calli were induced from 20 day old aseptic seedling explants on Murashige and Skoog medium fortified with various concentrations and combinations of plant growth regulators, benzylaminopurine (BAP), α-naphthaleneacetic acid (NAA), indole 3-butyric acid (IBA) and gibberellic acid (GA3). A mean of 8.6 shoots developed from organogenic callus induced from a 2 x 2 cm leaf explants on MS medium with 3% sucrose having 5.37μM NAA in combination with 2.22 μM BAP with 60% induction capacity. Further development of adventitious shoots could be achieved by sub culturing the callus to the same medium with 4.40 μM BAP and 0.288 μM GA3. Organogenesis could not be achieved from the calli of ex vitro derived leaf explants. The developed shoots were rooted on half-strength MS medium with 1% sucrose, 4.90 μM IBA and 0.93 μM kinetin at a frequency of 85%. Well rooted plantlets were then transplanted to vermiculite soil (3:1) mixture in polythene covered pots kept under culture room conditions. Approximately 60% plantlets survived and grew into whole plants.Keywords: Adventitious shoots, caulogenesis, organogenic callus-histologyAfrican Journal of Biotechnology, Vol. 13(1), pp. 55-60, 1 January, 201

Analysis of spontaneous individual case safety reports reported at adverse drug reaction monitoring centre: tertiary care teaching hospital in South India

Background: Drugs are double edged weapons, they are used in treatment of the patients but in return can harm as well. The safety of drug prescribing has become a need of the hour topic in medicine. Safety monitoring of patients via Pharmacovigilance tool has become an integral part of pharmacotherapy. This study has been undertaken to analyze the various individual case safety reports including the Special situation cases of medicational error and over dose and to promote the reporting of adverse drug reactions (ADRs) among the healthcare professionals (HCPs).Methods: A retrospective non-interventional observational study was done for indexed period of six months at AMC-PvPI under Osmania Medical College and General Hospital. The reported individual case safety reports (ICSRs) are evaluated on basis of demographics of age and gender, seriousness criteria, outcome parameters and causality assessment of suspected drug (s) and suspected ADR/AE (s) as per the ICH guidelines and WHO causality assessment scale.Results: A total of 177 ICSRs are reported out of that 137 were ADRs, 36-medication error cases and 4-cases of over dose. The incidence of ADRs in females are high compared with males was identical. The occurrence of ADRs in adult patients (61%) was significantly higher than other age groups. Of total ADRs, most of them were with analgesics (26%) and highly reported system organ classification was CNS. Overall, 79% patients were recovered from ADRs.Conclusions: The results depicted an insight to the HCPs on the importance of monitoring and reporting of ICSRs. Our study results emphasized need to roll out a pharmacovigilance practice tool to ensure the safe use of drugs for better Pharmacotherapy and development of pharmacogenomic studies

A solvable class of quadratic 0–1 programming

AbstractWe show that the minimum of the pseudo-Boolean quadratic function ƒ(x) = xTQx + cTx can be found in linear time when the graph defined by Q is transformable into a combinatorial circuit of AND, OR, NAND, NOR or NOT logic gates. A novel modeling technique is used to transform the graph defined by Q into a logic circuit. A consistent labeling of the signals in the logic circuit from the set {0, 1} corresponds to the global minimum of ƒ and the labeling is determined through logic simulation of the circuit. Our approach establishes a direct and constructive relationship between pseudo-Boolean functions and logic circuits.In the restricted case when all the elements of Q are nonpositive, the minimum of ƒ can be obtained in polynomial time [15]. We show that the problem of finding the minimum of ƒ, even in the special case when all the elements of Q are positive, is NP-complete

A RETROSPECTIVE STUDY TO EVALUATE THE EFFICACY OF INJECTION AUGMENTIN IN COVID-19 PATIENTS WITH PNEUMONIA AT A TERTIARY CARE TEACHING HOSPITAL, TELANGANA

Objective: Coronavirus is a single-stranded, enveloped, positive-sense RNA virus. It is responsible for the acute respiratory syndrome (SARS) and the disease is named COVID-19 by WHO. It is also called SARS-CoV-2. Pneumonia is one of the complications of COVID-19 disease. Patients with pneumonia admitted to General Hospital were treated with Augmentin. Augmentin is a broad-spectrum antibacterial that has been available for clinical use in a wide range of indications for over 20 y and is now used primarily in the treatment of respiratory tract infections. The main objective of our study is to evaluate the efficacy of Augmentin in COVID-19 patients with pneumonia in terms of beneficial effects after treatment. Methods: The present study was a retrospective, observational, record-based study of the case sheets of COVID-19 patients with pneumonia. The statistical analysis was done using paired t-test. Results: In our institution COVID-19 patients with pneumonia were treated with Tablet FAVIRAPIR and Injection AUGMENTIN. The results were calculated using paired t-test and the P-value was<0.0001, which is significant as it is less than 0.05. Therefore, the post-treatment outcome results showed a significant improvement in disease reduction. Conclusion: The study concludes that the empirical treatment of COVID-19 patients with pneumonia using an appropriate antibiotic reduces further deterioration of patients with pneumonia due to complications and also protects the patients from acquired infections during the hospital stay

Deep Learning-Based Real-Time Quality Control of Standard Video Compression for Live Streaming

Ensuring high-quality video content for wireless users has become increasingly vital. Nevertheless, maintaining a consistent level of video quality faces challenges due to the fluctuating encoded bitrate, primarily caused by dynamic video content, especially in live streaming scenarios. Video compression is typically employed to eliminate unnecessary redundancies within and between video frames, thereby reducing the required bandwidth for video transmission. The encoded bitrate and the quality of the compressed video depend on encoder parameters, specifically, the quantization parameter (QP). Poor choices of encoder parameters can result in reduced bandwidth efficiency and high likelihood of non-conformance. Non-conformance refers to the violation of the peak signal-to-noise ratio (PSNR) constraint for an encoded video segment. To address these issues, a real-time deep learning-based H.264 controller is proposed. This controller dynamically estimates the optimal encoder parameters based on the content of a video chunk with minimal delay. The objective is to maintain video quality in terms of PSNR above a specified threshold while minimizing the average bitrate of the compressed video. Experimental results, conducted on both QCIF dataset and a diverse range of random videos from public datasets, validate the effectiveness of this approach. Notably, it achieves improvements of up to 2.5 times in average bandwidth usage compared to the state-of-the-art adaptive bitrate video streaming, with a negligible non-conformance probability below $10^{-2}$.Comment: arXiv admin note: text overlap with arXiv:2310.0685

Semantic Multi-Resolution Communications

Deep learning based joint source-channel coding (JSCC) has demonstrated significant advancements in data reconstruction compared to separate source-channel coding (SSCC). This superiority arises from the suboptimality of SSCC when dealing with finite block-length data. Moreover, SSCC falls short in reconstructing data in a multi-user and/or multi-resolution fashion, as it only tries to satisfy the worst channel and/or the highest quality data. To overcome these limitations, we propose a novel deep learning multi-resolution JSCC framework inspired by the concept of multi-task learning (MTL). This proposed framework excels at encoding data for different resolutions through hierarchical layers and effectively decodes it by leveraging both current and past layers of encoded data. Moreover, this framework holds great potential for semantic communication, where the objective extends beyond data reconstruction to preserving specific semantic attributes throughout the communication process. These semantic features could be crucial elements such as class labels, essential for classification tasks, or other key attributes that require preservation. Within this framework, each level of encoded data can be carefully designed to retain specific data semantics. As a result, the precision of a semantic classifier can be progressively enhanced across successive layers, emphasizing the preservation of targeted semantics throughout the encoding and decoding stages. We conduct experiments on MNIST and CIFAR10 dataset. The experiment with both datasets illustrates that our proposed method is capable of surpassing the SSCC method in reconstructing data with different resolutions, enabling the extraction of semantic features with heightened confidence in successive layers. This capability is particularly advantageous for prioritizing and preserving more crucial semantic features within the datasets

A cross-sectional, questionnaire-based study on knowledge, attitude, and practice of pharmacovigilance among post-graduates at a tertiary care teaching hospital, Telangana

Background: Pharmacovigilance is the process of drug safety monitoring that improves patients' quality of life through the collection and analysis of Adverse Drug Reactions (ADRs). In our state, most of the ADRs are reported by a spontaneous reporting system of individual cases from health care professionals to Adverse Drug Reaction Monitoring Centre (AMC) under the Pharmacovigilance Programme of India (PvPI). Post-graduates (PGs) play a vital role in reporting ADRs as they are in personal evidence with all events after drug administration. The main objective of our study is to evaluate the Knowledge, Attitude, and Practice of Pharmacovigilance among post-graduates.Methods: The present study was a cross-sectional questionnaire-based study on knowledge, attitude, and practice (KAP) of Pharmacovigilance among 150 post-graduates at a tertiary care teaching hospital, Telangana. The statistical analysis was done using Statistical Package for Social Sciences (SPSS) version 25 software.Results: The results showed that there is relatively less knowledge among postgraduates. Attitude and practice-based questions evidenced a paradigm shift towards the construction of an organized Pharmacovigilance system. This study also highlights the under-reporting and the interventions needed to improve spontaneous reporting of ADRs.Conclusions: The knowledge of Pharmacovigilance with a positive attitude and practice among post-graduates is essential for reporting ADRs and reducing under-reporting.ng

BIG DATA ANALYTICS IN PHARMACOVIGILANCE - A GLOBAL TREND

Big data analysis has enhanced its demand nowadays in various sectors of health-care including pharmacovigilance. The exact definition of big data is not known to many people though it is routinely used by them. Big data refer to immense and voluminous computerized medical information which are obtained from electronic health records, administrative data, registries related to disease, drug monitoring, etc. This data are usually collected from doctors and pharmacists in a health-care facility. Analysis of big data in pharmacovigilance is useful for early raising of safety alerts, line listing them for signal detection of drugs and vaccines, and also for their validation. The present paper is intended to discuss big data analytics in pharmacovigilance focusing on global prospect and domestic country-India

Identification and validation of an allele specific marker associated with pungency in Capsicum spp.

Pungency or heat in Capsicum spp. is due to the accumulation of unique secondary compounds known as capsaicinoids in their placental tissues. Detecting presence or absence of pungency at the nursery stage is a challenging task in CMS based hybrid pepper breeding programs. In this study a DNA sequence possibly related to pungency trait with high similarity to Pun1 or At3 gene was investigated. Nucleotide alignment of the obtained sequences and corresponding fragment from the data base has revealed a 16bp deletion in C.annuum ‘Maor’. A multiplex agarose based co-dominant marker was designed to detect the identified polymorphism and named it as Cen1. This Cen1 marker is validated in a panel of 27 pepper genotypes belonging to C.annuum, C.chinensis, C.frutescens and C.baccatum for its wide utility. All these Capsicum accessions were correctly discriminated with phenotype. In addition, the ability of Cen1 marker to discriminate homozygous and heterozygous plants was demonstrated in F1 hybrids crossed from a non pungent ‘Maor’ and a pungent ‘Habanero’. The Cen1 marker was also associated with phenotypic character in the tested genotypes. Moreover the linkage association of Cen1 with At3 or Pun1 gene has also been discussed. Therefore the developed functional marker in this study will be highly useful in marker assisted selection (MAS) programmes, germplasam characterization and seed purity testing of chill