Behavioral profiling for adaptive video summarization: From generalization to personalization.

In today's world of managing multimedia content, dealing with the amount of CCTV footage poses challenges related to storage, accessibility and efficient navigation. To tackle these issues, we suggest an encompassing technique, for summarizing videos that merges machine-learning techniques with user engagement. Our methodology consists of two phases, each bringing improvements to video summarization. In Phase I we introduce a method for summarizing videos based on keyframe detection and behavioral analysis. By utilizing technologies like YOLOv5 for object recognition, Deep SORT for object tracking, and Single Shot Detector (SSD) for creating video summaries. In Phase II we present a User Interest Based Video summarization system driven by machine learning. By incorporating user preferences into the summarization process we enhance techniques with personalized content curation. Leveraging tools such as NLTK, OpenCV, TensorFlow, and the EfficientDET model enables our system to generate customized video summaries tailored to preferences. This innovative approach not only enhances user interactions but also efficiently handles the overwhelming amount of video data on digital platforms. By combining these two methodologies we make progress in applying machine learning techniques while offering a solution to the complex challenges presented by managing multimedia data.

Application of the Nadaraya-Watson estimator based attention mechanism to the field of predictive maintenance.

Attention mechanism has recently gained immense importance in the natural language processing (NLP) world. This technique highlights parts of the input text that the NLP task (such as translation) must pay "attention" to. Inspired by this, some researchers have recently applied the NLP domain, deep-learning based, attention mechanism techniques to predictive maintenance. In contrast to the deep-learning based solutions, Industry 4.0 predictive maintenance solutions that often rely on edge-computing, demand lighter predictive models. With this objective, we have investigated the adaptation of a simpler, incredibly fast and compute-resource friendly, "Nadaraya-Watson estimator based" attention method. We develop a method to predict tool-wear of a milling machine using this attention mechanism and demonstrate, with the help of heat-maps, how the attention mechanism highlights regions that assist in predicting onset of tool-wear. We validate the effectiveness of this adaptation on the benchmark IEEEDataPort PHM Society dataset, by comparing against other comparatively "lighter" machine learning techniques - Bayesian Ridge, Gradient Boosting Regressor, SGD Regressor and Support Vector Regressor. Our experiments indicate that the proposed Nadaraya-Watson attention mechanism performed best with an MAE of 0.069, RMSE of 0.099 and R2 of 83.40 %, when compared to the next best technique Gradient Boosting Regressor with figures of 0.100, 0.138, 66.51 % respectively. Additionally, it produced a lighter and faster model as well.•We propose a Nadaraya-Watson estimator based "attention mechanism", applied to a predictive maintenance problem.•Unlike the deep-learning based attention mechanisms from the NLP domain, our method creates fast, light and high-performance models, suitable for edge computing devices and therefore supports the Industry 4.0 initiative.•Method validated on real tool-wear data of a milling machine.

Detecting health misinformation: A comparative analysis of machine learning and graph convolutional networks in classification tasks.

In the digital age, the proliferation of health-related information online has heightened the risk of misinformation, posing substantial threats to public well-being. This research conducts a meticulous comparative analysis of classification models, focusing on detecting health misinformation. The study evaluates the performance of traditional machine learning models and advanced graph convolutional networks (GCN) across critical algorithmic metrics. The results comprehensively understand each algorithm's effectiveness in identifying health misinformation and provide valuable insights for combating the pervasive spread of false health information in the digital landscape. GCN with TF-IDF gives the best result, as shown in the result section. •The research method involves a comparative analysis of classification algorithms to detect health misinformation, exploring traditional machine learning models and graph convolutional networks.•This research used algorithms such as Passive Aggressive Classifier, Random Forest, Decision Tree, Logistic Regression, Light GBM, GCN, GCN with BERT, GCN with TF-IDF, and GCN with Word2Vec were employed. Performance Metrics: Accuracy: for Passive Aggressive Classifier: 85.75 %, Random Forest: 86 %, Decision Tree: 81.30 %, Light BGM: 83.29 %, normal GCN: 84.53 %, GCN with BERT: 85.00 %, GCN with TR-IDF: 93.86 % and GCN with word2Vec: 81.00 %•Algorithmic performance metrics, including accuracy, precision, recall, and F1-score, were systematically evaluated to assess the efficacy of each model in detecting health misinformation, focusing on understanding the strengths and limitations of different approaches. The superior performance of Graph Convolutional Networks (GCNs) with TF-IDF embedding, achieving an accuracy of 93.86.

A comparison of deep transfer learning backbone architecture techniques for printed text detection of different font styles from unstructured documents.

Object detection methods based on deep learning have been used in a variety of sectors including banking, healthcare, e-governance, and academia. In recent years, there has been a lot of attention paid to research endeavors made towards text detection and recognition from different scenesor images of unstructured document processing. The article's novelty lies in the detailed discussion and implementation of the various transfer learning-based different backbone architectures for printed text recognition. In this research article, the authors compared the ResNet50, ResNet50V2, ResNet152V2, Inception, Xception, and VGG19 backbone architectures with preprocessing techniques as data resizing, normalization, and noise removal on a standard OCR Kaggle dataset. Further, the top three backbone architectures selected based on the accuracy achieved and then hyper parameter tunning has been performed to achieve more accurate results. Xception performed well compared with the ResNet, Inception, VGG19, MobileNet architectures by achieving high evaluation scores with accuracy (98.90%) and min loss (0.19). As per existing research in this domain, until now, transfer learning-based backbone architectures that have been used on printed or handwritten data recognition are not well represented in literature. We split the total dataset into 80 percent for training and 20 percent for testing purpose and then into different backbone architecture models with the same number of epochs, and found that the Xception architecture achieved higher accuracy than the others. In addition, the ResNet50V2 model gave us higher accuracy (96.92%) than the ResNet152V2 model (96.34%).

Knockdown of Tlr3 in dorsal striatum reduces ethanol consumption and acute functional tolerance in male mice.

Systemic activation of toll-like receptor 3 (TLR3) signaling using poly(I:C), a TLR3 agonist, drives ethanol consumption in several rodent models, while global knockout of Tlr3 reduces drinking in C57BL/6J male mice. To determine if brain TLR3 pathways are involved in drinking behavior, we used CRISPR/Cas9 genome editing to generate a Tlr3 floxed (Tlr3F/F) mouse line. After sequence confirmation and functional validation of Tlr3 brain transcripts, we injected Tlr3F/F male mice with an adeno-associated virus expressing Cre recombinase (AAV5-CMV-Cre-GFP) to knockdown Tlr3 in the medial prefrontal cortex, nucleus accumbens, or dorsal striatum (DS). Only Tlr3 knockdown in the DS decreased two-bottle choice, every-other-day (2BC-EOD) ethanol consumption. DS-specific deletion of Tlr3 also increased intoxication and prevented acute functional tolerance to ethanol. In contrast, poly(I:C)-induced activation of TLR3 signaling decreased intoxication in male C57BL/6J mice, consistent with its ability to increase 2BC-EOD ethanol consumption in these mice. We also found that TLR3 was highly colocalized with DS neurons. AAV5-Cre transfection occurred predominantly in neurons, but there was minimal transfection in astrocytes and microglia. Collectively, our previous and current studies show that activating or inhibiting TLR3 signaling produces opposite effects on acute responses to ethanol and on ethanol consumption. While previous studies, however, used global knockout or systemic TLR3 activation (which alter peripheral and brain innate immune responses), the current results provide new evidence that brain TLR3 signaling regulates ethanol drinking. We propose that activation of TLR3 signaling in DS neurons increases ethanol consumption and that a striatal TLR3 pathway is a potential target to reduce excessive drinking.

Lentiviral mediated delivery of CRISPR/Cas9 reduces intraocular pressure in a mouse model of myocilin glaucoma.

Mutations in myocilin (MYOC) are the leading known genetic cause of primary open-angle glaucoma, responsible for about 4% of all cases. Mutations in MYOC cause a gain-of-function phenotype in which mutant myocilin accumulates in the endoplasmic reticulum (ER) leading to ER stress and trabecular meshwork (TM) cell death. Therefore, knocking out myocilin at the genome level is an ideal strategy to permanently cure the disease. We have previously utilized CRISPR/Cas9 genome editing successfully to target MYOC using adenovirus 5 (Ad5). However, Ad5 is not a suitable vector for clinical use. Here, we sought to determine the efficacy of adeno-associated viruses (AAVs) and lentiviruses (LVs) to target the TM. First, we examined the TM tropism of single-stranded (ss) and self-complimentary (sc) AAV serotypes as well as LV expressing GFP via intravitreal (IVT) and intracameral (IC) injections. We observed that LV_GFP expression was more specific to the TM injected via the IVT route. IC injections of Trp-mutant scAAV2 showed a prominent expression of GFP in the TM. However, robust GFP expression was also observed in the ciliary body and retina. We next constructed lentiviral particles expressing Cas9 and guide RNA (gRNA) targeting MYOC (crMYOC) and transduction of TM cells stably expressing mutant myocilin with LV_crMYOC significantly reduced myocilin accumulation and its associated chronic ER stress. A single IVT injection of LV_crMYOC in Tg-MYOCY437H mice decreased myocilin accumulation in TM and reduced elevated IOP significantly. Together, our data indicates, LV_crMYOC targets MYOC gene editing in TM and rescues a mouse model of myocilin-associated glaucoma.

Enhancement of handwritten text recognition using AI-based hybrid approach.

Handwritten text recognition (HTR) within computer vision and image processing stands as a prominent and challenging research domain, holding significant implications for diverse applications. Among these, it finds usefulness in reading bank checks, prescriptions, and deciphering characters on various forms. Optical character recognition (OCR) technology, specifically tailored for handwritten documents, plays a pivotal role in translating characters from a range of file formats, encompassing both word and image documents. Challenges in HTR encompass intricate layout designs, varied handwriting styles, limited datasets, and less accuracy achieved. Recent advancements in Deep Learning and Machine Learning algorithms, coupled with the vast repositories of unprocessed data, have propelled researchers to achieve remarkable progress in HTR. This paper aims to address the challenges in handwritten text recognition by proposing a hybrid approach. The primary objective is to enhance the accuracy of recognizing handwritten text from images. Through the integration of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) with a Connectionist Temporal Classification (CTC) decoder, the results indicate substantial improvement. The proposed hybrid model achieved an impressive 98.50% and 98.80% accuracy on the IAM and RIMES datasets, respectively. This underscores the potential and efficacy of the consecutive use of these advanced neural network architectures in enhancing handwritten text recognition accuracy. •The proposed method introduces a hybrid approach for handwritten text recognition, employing CNN and BiLSTM with CTC decoder.•Results showcase a remarkable accuracy improvement of 98.50% and 98.80% on IAM and RIMES datasets, emphasizing the potential of this model for enhanced accuracy in recognizing handwritten text from images.

Multimodal Abstractive Summarization using bidirectional encoder representations from transformers with attention mechanism.

In recent decades, abstractive text summarization using multimodal input has attracted many researchers due to the capability of gathering information from various sources to create a concise summary. However, the existing methodologies based on multimodal summarization provide only a summary for the short videos and poor results for the lengthy videos. To address the aforementioned issues, this research presented the Multimodal Abstractive Summarization using Bidirectional Encoder Representations from Transformers (MAS-BERT) with an attention mechanism. The purpose of the video summarization is to increase the speed of searching for a large collection of videos so that the users can quickly decide whether the video is relevant or not by reading the summary. Initially, the data is obtained from the publicly available How2 dataset and is encoded using the Bidirectional Gated Recurrent Unit (Bi-GRU) encoder and the Long Short Term Memory (LSTM) encoder. The textual data which is embedded in the embedding layer is encoded using a bidirectional GRU encoder and the features with audio and video data are encoded with LSTM encoder. After this, BERT based attention mechanism is used to combine the modalities and finally, the BI-GRU based decoder is used for summarizing the multimodalities. The results obtained through the experiments that show the proposed MAS-BERT has achieved a better result of 60.2 for Rouge-1 whereas, the existing Decoder-only Multimodal Transformer (D-MmT) and the Factorized Multimodal Transformer based Decoder Only Language model (FLORAL) has achieved 49.58 and 56.89 respectively. Our work facilitates users by providing better contextual information and user experience and would help video-sharing platforms for customer retention by allowing users to search for relevant videos by looking at its summary.

Advances in the co-production of biosurfactant and other biomolecules: statistical approaches for process optimization.

An efficient microbial conversion for simultaneous synthesis of multiple high-value compounds, such as biosurfactants and enzymes, is one of the most promising aspects for an economical bioprocess leading to a marked reduction in production cost. Although biosurfactant and enzyme production separately have been much explored, there are limited reports on the predictions and optimization studies on simultaneous production of biosurfactants and other industrially important enzymes, including lipase, protease, and amylase. Enzymes are suited for an integrated production process with biosurfactants as multiple common industrial processes and applications are catalysed by these molecules. However, the complexity in microbial metabolism complicates the production process. This study details the work done on biosurfactant and enzyme co-production and explores the application and scope of various statistical tools and methodologies in this area of research. The use of advanced computational tools is yet to be explored for the optimization of downstream strategies in the co-production process. Given the complexity of the co-production process and with various new methodologies based on artificial intelligence (AI) being invented, the scope of AI in shaping the biosurfactant-enzyme co-production process is immense and would lead to not only efficient and rapid optimization, but economical extraction of multiple biomolecules as well.

Comparative Evaluation of Relapse After LeFort 1 Osteotomy in Cleft Palate and Non-cleft Individuals: A Longitudinal Study.

PURPOSE: The aim of this study was to assess and compare the likelihood of relapse one year after LeFort I advancement surgery in patients with and without cleft lip and palate. METHODS: A retrospective observational study which included two groups of participants who underwent LeFort I maxillary advancement was performed. Group 1 included 10 non-cleft subjects and Group 2 included 21 subjects with cleft palate. These maxillary deficient patients were chosen and operated using a technique where only a sagittal displacement was intended. Patients who underwent additional mandibular surgery, significant vertical or transverse alterations, or both were excluded. Pre-operative (T1), immediately post-operative (T2), and minimum one-year follow-up (T3) lateral cephalograms were studied for each group. Skeletal stability and dental stability after LeFort I surgery at a minimum of one-year follow-up in cleft palate and non-cleft patients were evaluated. RESULTS: For the given sample size, relapse tendencies showed statistically significant differences between cleft palate patients and non-cleft palate patients after maxillary advancement. The sella nasion angle and horizontal overlap of the maxillary and mandibular incisors (overjet) decreased by 2 degrees and 0.9 mm respectively in the cleft palate group while decreasing by 1.10 degrees and 0.40 mm in the non-cleft group. CONCLUSIONS: After maxillary advancement with LeFort I osteotomy and miniplate fixation in patients with cleft palate and non-cleft patients, some degree of relapse was detected in both groups for the given sample size after one year post-operatively. The cleft palate group displayed additional relapse tendencies when compared to the non-cleft group.

Performance analysis of hybrid deep learning framework using a vision transformer and convolutional neural network for handwritten digit recognition.

Digitization created a demand for highly efficient handwritten document recognition systems. A handwritten document consists of digits, text, symbols, diagrams, etc. Digits are an essential element of handwritten documents. Accurate recognition of handwritten digits is vital for effective communication and data analysis. Various researchers have attempted to address this issue with modern convolutional neural network (CNN) techniques. Even after training, CNN filter weights remain unchanged despite the high identification accuracy. As a result, the process cannot flexibly adapt to input changes. Hence computer vision researchers have recently become interested in Vision Transformers (ViTs) and Multilayer Perceptrons (MLPs). The shortcomings of CNNs gave rise to a hybrid model revolution that combines the best elements of the two fields. This paper analyzes how the hybrid convolutional ViT model affects the ability to recognize handwritten digits. Also, the real-time data contains noise, distortions, and varying writing styles. Hence, cleaned and uncleaned handwritten digit images are used for evaluation in this paper. The accuracy of the proposed method is compared with the state-of-the-art techniques, and the result shows that the proposed model achieves the highest recognition accuracy. Also, the probable solutions for recognizing other aspects of handwritten documents are discussed in this paper.•Analyzed the effect of convolutional vision transformer on cleaned and real-time handwritten digit images.•The model's performance improved with the implication of cross-validation and hyper-parameter tuning.•The results show that the proposed model is robust, feasible, and effective on cleaned and uncleaned handwritten digits.

Effects of the WHO Labour Care Guide on cesarean section in India: a pragmatic, stepped-wedge, cluster-randomized pilot trial.

Cesarean section rates worldwide are rising, driven by medically unnecessary cesarean use. The new World Health Organization Labour Care Guide (LCG) aims to improve the quality of care for women during labor and childbirth. Using the LCG might reduce overuse of cesarean; however, its effects have not been evaluated in randomized trials. We conducted a stepped-wedge, cluster-randomized pilot trial in four hospitals in India to evaluate the implementation of an LCG strategy intervention, compared with routine care. We performed this trial to pilot the intervention and obtain preliminary effectiveness data, informing future research. Eligible clusters were four hospitals with >4,000 births annually and cesarean rates ≥30%. Eligible women were those giving birth at ≥20 weeks' gestation. One hospital transitioned to intervention every 2 months, according to a random sequence. The primary outcome was the cesarean rate among women in Robson Group 1 (that is, those who were nulliparous and gave birth to a singleton, term pregnancy in cephalic presentation and in spontaneous labor). A total of 26,331 participants gave birth. A 5.5% crude absolute reduction in the primary outcome was observed (45.2% versus 39.7%; relative risk 0.85, 95% confidence interval 0.54-1.33). Maternal process-of-care outcomes were not significantly different, though labor augmentation with oxytocin was 18.0% lower with the LCG strategy. No differences were observed for other health outcomes or women's birth experiences. These findings can guide future definitive effectiveness trials, particularly in settings where urgent reversal of rising cesarean section rates is needed. Clinical Trials Registry India number: CTRI/2021/01/030695 .

Anomaly-informed remaining useful life estimation (AIRULE) of bearing machinery using deep learning framework.

A rolling bearing is a crucial element within rotating machinery, and its smooth operation profoundly influences the overall well-being of the equipment. Consequently, analyzing its operational condition is crucial to prevent production losses or, in extreme cases, potential fatalities due to catastrophic failures. Accurate estimates of the Remaining Useful Life (RUL) of rolling bearings ensure manufacturing safety while also leading to cost savings.•This paper proposes an intelligent deep learning-based framework for remaining useful life estimation of bearings on the basis of informed detection of anomalies.•The paper demonstrates the setup of an experimental bearing test rig and the collection of bearing condition monitoring data such as vibration data.•Advanced hybrid models of Encoder-Decoder LSTM demonstrate high forecasting accuracy in RUL estimation.

Correlation of clinical features with neuroimaging in children with mild head injury.

Objectives: Mild head injury is defined as a pediatric Glasgow Coma Scale (GCS) score of 13-15 at admission following head trauma. There are no clear indications for neuroimaging in such children. The aim of our study was to analyze the correlation of symptoms commonly encountered following mild head injury with any abnormality on the computed tomography (CT) scan. Materials and Methods: This is a retrospective and observational study done in a tertiary care hospital. Records of all the children fulfilling the inclusion criteria were retrieved. Demographic details such as age, gender, and type of injury (hit by a blunt object, fall from height, and road traffic accident), symptoms such as presence and number of episodes of vomiting, presence and duration of loss of consciousness (LOC), presence of drowsiness, headache, giddiness, seizures, focal deficits and the GCS score, and CT scan findings were noted in a predesigned proforma. A CT scan with any of the following abnormalities such as skull fracture, cerebral edema, intracranial bleed (epidural hematoma, subdural hematoma, intracerebral hematoma, and subarachnoid hemorrhage), and cerebral contusion was considered as abnormal. Any neurosurgical intervention done was also noted. Results: A total of 134 children in the age group of 1 month to 18 years with mild head injury were included in this study. The median (interquartile range) age of the children was 5 (2, 8) years, with majority being males. Road traffic accidents (34.3%) were the most common cause of injury, followed by fall from lesser than 3 feet height (31.3%). The most common symptoms were vomiting (43.6%), scalp/facial abrasions (37.2%), and LOC (31.9%). CT scan was abnormal in 53.7% of the cases, with skull fracture (35.1%) being the most common finding, followed by cerebral edema (13.4%). Among all the symptoms, ear/nosebleed or cerebrospinal fluid (CSF) otorrhea/rhinorrhea had a statistically significant association with a positive CT scan with P = 0.05 and an odds ratio of 1.4 (95% confidence interval, 1-1.9). Conclusion: Children with mild head injury presenting with clinical features such as bleeding from the ear or nose and CSF otorrhea/rhinorrhea are more likely to have an abnormal CT scan. Hence, such children require close neurological observation and prompt intervention if necessary. However, abnormality on CT scan cannot be reliably ruled out based on the symptoms alone.

Artificial intelligence: Machine learning approach for screening large database and drug discovery.

Recent research in drug discovery dealing with many faces difficulties, including development of new drugs during disease outbreak and drug resistance due to rapidly accumulating mutations. Virtual screening is the most widely used method in computer aided drug discovery. It has a prominent ability in screening drug targets from large molecular databases. Recently, a number of web servers have developed for quickly screening publicly accessible chemical databases. In a nutshell, deep learning algorithms and artificial neural networks have modernised the field. Several drug discovery processes have used machine learning and deep learning algorithms, including peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modelling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Although there are presently a wide variety of data-driven AI/ML tools available, the majority of these tools have, up to this point, been developed in the context of non-communicable diseases like cancer, and a number of obstacles have prevented the translation of these tools to the discovery of treatments against infectious diseases. In this review various aspects of AI and ML in virtual screening of large databases were discussed. Here, with an emphasis on antivirals as well as other disease, offers a perspective on the advantages, drawbacks, and hazards of AI/ML techniques in the search for innovative treatments.

Development of a three-dimensional organoid model to explore early retinal phenotypes associated with Alzheimer's disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by the accumulation of Aß plaques and neurofibrillary tangles, resulting in synaptic loss and neurodegeneration. The retina is an extension of the central nervous system within the eye, sharing many structural similarities with the brain, and previous studies have observed AD-related phenotypes within the retina. Three-dimensional retinal organoids differentiated from human pluripotent stem cells (hPSCs) can effectively model some of the earliest manifestations of disease states, yet early AD-associated phenotypes have not yet been examined. Thus, the current study focused upon the differentiation of hPSCs into retinal organoids for the analysis of early AD-associated alterations. Results demonstrated the robust differentiation of retinal organoids from both familial AD and unaffected control cell lines, with familial AD retinal organoids exhibiting a significant increase in the Aß42:Aß40 ratio as well as phosphorylated Tau protein, characteristic of AD pathology. Further, transcriptional analyses demonstrated the differential expression of many genes and cellular pathways, including those associated with synaptic dysfunction. Taken together, the current study demonstrates the ability of retinal organoids to serve as a powerful model for the identification of some of the earliest retinal alterations associated with AD.

Enhancing the Breast Histopathology Image Analysis for Cancer Detection Using Variational Autoencoder.

A breast tissue biopsy is performed to identify the nature of a tumour, as it can be either cancerous or benign. The first implementations involved the use of machine learning algorithms. Random Forest and Support Vector Machine (SVM) were used to classify the input histopathological images into whether they were cancerous or non-cancerous. The implementations continued to provide promising results, and then Artificial Neural Networks (ANNs) were applied for this purpose. We propose an approach for reconstructing the images using a Variational Autoencoder (VAE) and the Denoising Variational Autoencoder (DVAE) and then use a Convolutional Neural Network (CNN) model. Afterwards, we predicted whether the input image was cancerous or non-cancerous. Our implementation provides predictions with 73% accuracy, which is greater than the results produced by our custom-built CNN on our dataset. The proposed architecture will prove to be a new field of research and a new area to be explored in the field of computer vision using CNN and Generative Modelling since it incorporates reconstructions of the original input images and provides predictions on them thereafter.

Implementing the WHO Labour Care Guide to reduce the use of Caesarean section in four hospitals in India: protocol and statistical analysis plan for a pragmatic, stepped-wedge, cluster-randomized pilot trial.

BACKGROUND: The World Health Organization (WHO) Labour Care Guide (LCG) is a paper-based labour monitoring tool designed to facilitate the implementation of WHO's latest guidelines for effective, respectful care during labour and childbirth. Implementing the LCG into routine intrapartum care requires a strategy that improves healthcare provider practices during labour and childbirth. Such a strategy might optimize the use of Caesarean section (CS), along with potential benefits on the use of other obstetric interventions, maternal and perinatal health outcomes, and women's experience of care. However, the effects of a strategy to implement the LCG have not been evaluated in a randomised trial. This study aims to: (1) develop and optimise a strategy for implementing the LCG (formative phase); and (2) To evaluate the implementation of the LCG strategy compared with usual care (trial phase). METHODS: In the formative phase, we will co-design the LCG strategy with key stakeholders informed by facility assessments and provider surveys, which will be field tested in one hospital. The LCG strategy includes a LCG training program, ongoing supportive supervision from senior clinical staff, and audit and feedback using the Robson Classification. We will then conduct a stepped-wedge, cluster-randomized pilot trial in four public hospitals in India, to evaluate the effect of the LCG strategy intervention compared to usual care (simplified WHO partograph). The primary outcome is the CS rate in nulliparous women with singleton, term, cephalic pregnancies in spontaneous labour (Robson Group 1). Secondary outcomes include clinical and process of care outcomes, as well as women's experience of care outcomes. We will also conduct a process evaluation during the trial, using standardized facility assessments, in-depth interviews and surveys with providers, audits of completed LCGs, labour ward observations and document reviews. An economic evaluation will consider implementation costs and cost-effectiveness. DISCUSSION: Findings of this trial will guide clinicians, administrators and policymakers on how to effectively implement the LCG, and what (if any) effects the LCG strategy has on process of care, health and experience outcomes. The trial findings will inform the rollout of LCG internationally. TRIAL REGISTRATION: CTRI/2021/01/030695 (Protocol version 1.4, 25 April 2022).

The new WHO Labour Care Guide (LCG) is an innovative partograph that emphasises women-centred, evidence-based care during labour and childbirth. Together with clinicians working at four hospitals in India, we will develop and test a strategy to implement the LCG into routine care in labour wards of these hospitals. We will use a randomised trial design where this LCG strategy is introduced sequentially in each of the four hospitals, in a random order. We will collect data on all women giving birth and their newborns during this period and analyse whether the LCG strategy has any effects on the use of Caesarean section, women's and newborn's health outcomes, and women's experiences during labour and childbirth. While the trial is being conducted, we will also collect qualitative and quantitative data from doctors, nurses and midwives working in these hospitals, to understand their perspectives and experiences of using the LCG in their day-to-day work. In addition, we will collect economic data to understand how much the LCG strategy costs, and how much money it might save if it is effective. Through this study, our international collaboration will generate critical evidence and innovative tools to support implementation of the LCG in other countries.

Lentiviral mediated delivery of CRISPR/Cas9 reduces intraocular pressure in a mouse model of myocilin glaucoma.

Mutations in myocilin (MYOC) are the leading known genetic cause of primary open-angle glaucoma, responsible for about 4% of all cases. Mutations in MYOC cause a gain-of-function phenotype in which mutant myocilin accumulates in the endoplasmic reticulum (ER) leading to ER stress and trabecular meshwork (TM) cell death. Therefore, knocking out myocilin at the genome level is an ideal strategy to permanently cure the disease. We have previously utilized CRISPR/Cas9 genome editing successfully to target MYOC using adenovirus 5 (Ad5). However, Ad5 is not a suitable vector for clinical use. Here, we sought to determine the efficacy of adeno-associated viruses (AAVs) and lentiviruses (LVs) to target the TM. First, we examined the TM tropism of single-stranded (ss) and self-complimentary (sc) AAV serotypes as well as LV expressing GFP via intravitreal (IVT) and intracameral (IC) injections. We observed that LV_GFP expression was more specific to the TM injected via the IVT route. IC injections of Trp-mutant scAAV2 showed a prominent expression of GFP in the TM. However, robust GFP expression was also observed in the ciliary body and retina. We next constructed lentiviral particles expressing Cas9 and guide RNA (gRNA) targeting MYOC (crMYOC) and transduction of TM cells stably expressing mutant myocilin with LV_crMYOC significantly reduced myocilin accumulation and its associated chronic ER stress. A single IVT injection of LV_crMYOC in Tg-MYOCY437H mice decreased myocilin accumulation in TM and reduced elevated IOP significantly. Together, our data indicates, LV_crMYOC targets MYOC gene editing in TM and rescues a mouse model of myocilin-associated glaucoma.

Evaluation of the efficacy of sticky bone and concentrated growth factor membrane along with a coronally advanced flap as compared to coronally advanced flap alone in the treatment of Miller's Class I and Class II gingival recession defects.

Context: Gingival recessions are commonly seen in the dentally cognizant population as well as those with limited access to dental attention. When root coverage is planned, the ultimate goal is to obtain complete root coverage, thus restoring the lost gingival unit covering the root. Aims: To determine the efficacy of sticky bone and concentrated growth factor (CGF) membrane along with a coronally advanced flap (CAF) as compared to CAF alone in treating Miller's Class I and Class II gingival recessions (Cairo RT1). Settings and Design: The current study was a randomized double-blind controlled trial on 15 subjects using a split-mouth design. Materials and Methods: Fifteen subjects who were systemically healthy and had recession sites (30 sites) were randomly assigned to two groups: Group A (test group = CAF + CGF + sticky bone) and Group B (control group = CAF alone). Clinical outcome was assessed with parameters such as recession depth, recession width, keratinized gingival width, gingival mucosal thickness, and relative attachment level (RAL), and these were assessed at baseline and 1, 3, and 6 months. Results: A distinct improvement was observed in the depth and width of recession, RAL, keratinized gingival width, and mucosal thickness of the gingiva in the two groups from baseline to 6 months. Statistical significance was not seen on intergroup comparisons. Conclusions: Thus, clinical outcomes revealed noticeable improvement for both the groups. However, statistically, the efficacy of CGF and sticky bone was not perceived to be superior to that of CAF alone.