Dynamic circadian protein-protein interaction networks predict temporal organization of cellular functions.

Essentially all biological processes depend on protein-protein interactions (PPIs). Timing of such interactions is crucial for regulatory function. Although circadian (~24-hour) clocks constitute fundamental cellular timing mechanisms regulating important physiological processes, PPI dynamics on this timescale are largely unknown. Here, we identified 109 novel PPIs among circadian clock proteins via a yeast-two-hybrid approach. Among them, the interaction of protein phosphatase 1 and CLOCK/BMAL1 was found to result in BMAL1 destabilization. We constructed a dynamic circadian PPI network predicting the PPI timing using circadian expression data. Systematic circadian phenotyping (RNAi and overexpression) suggests a crucial role for components involved in dynamic interactions. Systems analysis of a global dynamic network in liver revealed that interacting proteins are expressed at similar times likely to restrict regulatory interactions to specific phases. Moreover, we predict that circadian PPIs dynamically connect many important cellular processes (signal transduction, cell cycle, etc.) contributing to temporal organization of cellular physiology in an unprecedented manner

CLINICAL EVALUATION OF EFFICACY OF KUSTHADI CHURNA WITH UDUMBARADI TAILA IN THE MANAGEMENT OF KARNINI YONIVYAPAD W.S.R. TO CERVICAL EROSION

Karnini yonivyapad is one of the gynaecological disorders described in Ayurveda under the umbrella of the Yonivyapad. According to the signs and symptoms, it is more nearer to the benign lesion cervical erosion, which occurs due to the replacement of the stratified squamous epithelium of the portio-vaginalis by the columnar epithelium of endocervix. The treatment is designed to destruct the columnar epithelium by any method and to promote the re-epithelization of the squamous tissues. Keeping this point in view, the present clinical trial is taken up with the aim of clinical evaluation of efficacy of Kusthadi churna with Udumbaradi taila in the management of Karnini yonivyapad (cervical erosion). Kusthadi churna along with Yonipichu with Udumbaradi taila group-A, and group-B only Udumbaradi taila yonipichu, and results were assessed on the basis of the epithelization of erosion and improvement in the symptoms. The study reveals that the mixed therapy group showed better results than the group of single drug

Exploration of two methods for quantitative Mitomycin C measurement in tumor tissue in vitro and in vivo

Two methods of quantifying Mitomycin C in tumor tissue are explored. A method of ultraviolet-visible absorption microscopy is developed and applied to measure the concentration of Mitomycin C in preserved mouse tumor tissue, as well as in gelatin samples. Concentrations as low as 60 μM can be resolved using this technique in samples that do not strongly scatter light. A novel method for monitoring the Mitomycin C concentrations inside a tumor is developed, based on microdialysis and ultraviolet-visible spectroscopy. A pump is used to perfuse a microdialysis probe with Ringer’s solution, which is fed to a flow cell to determine intratumor concentrations in real time to within a few μM. The success and limitations of these techniques are identified, and suggestions are made as to further development. To the authors’ knowledge these are the first attempts made to quantify Mitomycin C concentrations in tumor tissue

Relapsing severe Crohn’s colitis and perianal fistula on Infliximab maintenance therapy

Perianal fistula occurs in up to 43% of patients with Crohn’s disease. In Crohn’s disease it is important to identify the site of inflammation, area involved and based on that the treatment regimen is decided. Perianal abscesses must be drained and perianal fistulas must be treated with surgical and medical management. In this case report we would discuss about a patient who underwent failure with first line treatment and developed fistula. His symptoms decreased with due course of management and was advised for surgical procedure along with medical management

Multiple brain abscesses of odontogenic origin. May oral microbiota affect their development? a review of the current literature

In the last few years, the role of oral microbiota in the setting of oral diseases such as caries, periodontal disease, oral cancer and systemic infections, including rheumatoid arthritis, car-diovascular disease and brain abscess (BA), has attracted the attention of physicians and researchers. Approximately 5–7% of all BAs have an odontogenic origin, representing an important pathological systemic condition with a high morbidity and mortality. A systematic search of two databases (Pubmed and Ovid EMBASE) was performed for studies published up to 5 January 2021, reporting multiple BAs attributed to an odontogenic origin. According to PRISMA guidelines, we included a total of 16 papers reporting multiple BAs due to odontogenic infections. The aim of this review is to investigate the treatment modality and the clinical outcome of patients with multiple BAs due to odontogenic infections, as well as to identify the most common pathogens involved in this pathological status and their role, in the oral microbiota, in the onset of oral infections. A multidisciplinary approach is essential in the management of multiple BAs. Further studies are required to understand better the role of microbiota in the development of multiple BAs

Deep Learning Based Hate Speech Detection on Twitter

There have been growing worries about the effects of the widespread use of hate speech and harsh language on social media sites like Twitter. Effective strategies for recognising and reducing such dangerous material are necessary for resolving this problem. In this research, we give a detailed analysis of four deep learning models for identifying hate speech and inflammatory language on Twitter: the Long Short-Term Memory (LSTM), the Recurrent Neural Network (RNN), the Bidirectional LSTM (Bi-LSTM), and the Gated Recurrent Unit (GRU). We downloaded a large dataset from Kaggle that was curated for hate speech identification and used it in our experiment. We built each model after preprocessing and tokenization, then tweaked their hyperparameters for maximum efficiency. The models' abilities to detect hate speech were evaluated using standard measures including accuracy, precision, recall, and Fl-score. Our findings show that there is a wide range of effectiveness amongst models in terms of identifying hate speech and inflammatory language on Twitter. In terms of accuracy and Fl-scores, the Bi-LSTM and GRU models were superior to the LSTM and RNN. The results of this study imply that using bidirectional and gated processes may increase the models' capability of understanding the interdependencies and contexts of tweets, and hence, their classification accuracy

A Novel Hybrid Convolutional Neural Network- and Gated Recurrent Unit-Based Paradigm for IoT Network Traffic Attack Detection in Smart Cities

Internet of Things (IoT) devices within smart cities, require innovative detection methods. This paper addresses this critical challenge by introducing a deep learning-based approach for the detection of network traffic attacks in IoT ecosystems. Leveraging the Kaggle dataset, our model integrates Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs) to capture both spatial and sequential features in network traffic data. We trained and evaluated our model over ten epochs, achieving an impressive overall accuracy rate of 99%. The classification report reveals the model’s proficiency in distinguishing various attack categories, including ‘Normal’, ‘DoS’ (Denial of Service), ‘Probe’, ‘U2R’ (User to Root), and ‘Sybil’. Additionally, the confusion matrix offers valuable insights into the model’s performance across these attack types. In terms of overall accuracy, our model achieves an impressive accuracy rate of 99% across all attack categories. The weighted- average F1-score is also 99%, showcasing the model’s robust performance in classifying network traffic attacks in IoT devices for smart cities. This advanced architecture exhibits the potential to fortify IoT device security in the complex landscape of smart cities, effectively contributing to the safeguarding of critical infrastructur