Meenakshi Academy of Higher Education and Research

Study DesignUmbrella systematic review.ObjectiveTo qualitatively synthesise systematic reviews evaluating the prevalence, correlates, and outcomes of mental illnesses in individuals with traumatic spinal cord injury (tSCI).MethodsSystematic reviews reporting on depression, anxiety, post-traumatic stress disorder (PTSD), substance use disorders (SUD), cognitive impairment, and related psychological outcomes in tSCI populations were identified and synthesized as per PRISMA guidelines. Data on prevalence, risk factors, assessment tools, and interventions were extracted. Methodological quality was appraised using AMSTAR 2, and primary study overlap was assessed.ResultsTwenty systematic reviews published between 2005 and 2025 were included. Depression was the most frequently studied condition (n = 16), followed by anxiety (n = 10), PTSD (n = 6), and SUD (n = 4), with several reviews addressing multiple conditions. Primary study sample sizes ranged from 3152 to over 50000 participants, with wide variation in injury characteristics, study design, and outcome measures. Pooled prevalence estimates indicated a substantial burden: depression affected up to 43% of community-dwelling individuals, anxiety symptoms around 27%, PTSD up to 62%, and hazardous alcohol use ≥50% in some cohorts. Common risk factors included pain, injury severity, incomplete injury, low social support, maladaptive coping, and co-occurring psychological symptoms. Evidence for effective interventions was limited, and few studies used standardised, validated tools across settings.ConclusionsMental health conditions are highly prevalent in the tSCI population, yet intervention research remains limited. Standardised assessment, longitudinal designs, and targeted, evidence-based interventions are urgently needed to address this critical but under-recognised aspect of tSCI care.

Background & Objective: Endometriosis is characterized by the growth of endometrial-like tissue outside the uterus, leading to inflammation, pain, and infertility. Its pathogenesis involves genetic, immunological, and hormonal factors. This study examines the synergistic roles of IL‑16 and KRAS gene expression in endometriosis and their interaction with oxidative stress markers to identify potential biomarkers for targeted therapy.Materials & Methods: A case-control study was conducted with 300 subjects, including 150 cases diagnosed with endometriosis and 150 healthy controls. Gene expression levels of IL‑16 and KRAS were analyzed using real-time PCR. Oxidative stress markers, including Superoxide Dismutase (SOD), glutathione peroxidase, and vitamin C, along with the inflammatory cytokine IL‑6, were measured.Results: IL‑16 and KRAS gene expression levels were significantly elevated in cases compared with controls, with KRAS identified as an independent predictor (P=0.002). Oxidative stress markers demonstrated a marked reduction in SOD and glutathione peroxidase levels, accompanied by decreased vitamin C levels. Elevated IL‑6 levels (>5.57 pg/mL, OR=7.91, P=0.032) were associated with increased inflammation and oxidative stress related cellular damage.Conclusion: The findings indicate that IL‑16 and KRAS contribute to the progression of endometriosis through oxidative stress-mediated genetic alterations and inflammatory pathways. The study underscores the combined impact of oxidative imbalance, IL‑6 driven inflammation, and KRAS dysregulation in disease pathogenesis. Unlike previous research focusing on genetic polymorphisms, this study provides novel insights into gene expression patterns and their clinical implications. Further investigation into the mechanistic interactions among IL‑16, KRAS, and oxidative stress may aid in the development of targeted therapeutic strategies.

People around the world use social media to express and discuss their views and ideas on various situations, as well as socio-cultural and political affairs. Freedom of speech allows individuals to use social media platforms to create and spread hatred among people. Given its societal impact, identifying such posts on social media is crucial. A notable amount of research exists in this field, particularly in the English language; however, this needs to be extended to Indian languages. The proposed system attempts to identify hate speech in Dravidian language social media posts, specifically Tamil, using a custom dataset annotated based on the severity of hate speech in line with Article 19 of the Universal Declaration of Human Rights (UDHR). Due to the limited availability of resources, the proposed system adopts a few-shot learning approach for detecting hate speech. It employs a 4-way k-shot approach to classify Tamil texts into four class labels: ‘Lawful’, ‘May_be_Restricted’, ‘Must_be_Restricted’, and ‘Not_Hate’. Different sentence embeddings, such as Bidirectional Encoder Representations from Transformers (BERT)-base-multilingual, distiluse-base-multilingual, and language-agnostic embeddings, are used to capture contextual information during the hate speech detection process. The performance of the 4-way k-shot learning was examined for various values of k, and the analysis showed that the 4-way 80-shot learning with language-agnostic embeddings provided the best performance. The proposed system was compared with traditional and transformer-based models to evaluate its performance. Analysis of the effect of data augmentation on the performance of the 4-way 80-shot learning model using language-agnostic sentence embeddings showed a performance improvement, achieving an accuracy of 71.4% and a macro F1-score of 0.572. The proposed model also outperformed the existing methodologies for hate speech detection. All resources are made available through the GitHub repository ( https://github.com/JerinMahibha/HateSpeech-Severity DOI: 10.5281/zenodo.17116472 ).

Background: The Marginal mandibular nerve is a terminal branch of Facial nerve. The nerve runs close to the inferior margin of the mandible, superficial to the facial vessels and supplies the muscles of the lower lip. The proper knowledge on variations along the course of the nerve and its relations to facial vessels play a significant role in determining the success of any surgical intervention in that region. Materials and methods: This study is a cross-sectional study conducted in the department of anatomy of a private medical college in Tamil Nadu, India. Fifty facial halves from embalmed cadavers are utilised for this study. Descriptive statistics is used to analyse the data obtained from the study Results: In all the 50 specimens, marginal mandibular nerve emerged from the cervicofacial ramus of facial nerve. 26% specimens showed single branch of the nerve, in 56% specimens, two branches were observed, and in 18% specimens, three branches of marginal mandibular nerve were observed. 76% the nerve coursed inferior to mandible. The average distance of the nerve from the inferior margin of the mandible was 1.4 cm. In 96% of the specimens, the nerve coursed lateral to the facial vessels. In 4% of the specimen, the branches of the nerve were found both lateral and medial to the facial artery. Communication of the nerve with buccal branch was observed in 16% of specimens. Conclusion: As the marginal mandibular nerve shows lot of variations, a proper anatomical knowledge on the same helps the patients to have a good treatment preventing any iatrogenic complications. KEY WORDS: Marginal mandibular nerve, Facial artery, inferior margin of the mandible, Facial nerve.

Overall, the findings suggest that JA ethanolic extract could be an effective anticancer agent against HT-29 colon adenocarcinoma cells.

The print media is ever getting pressurized to produce output of high quality, customized output, shorter turn-around time, low operation cost and enhanced sustainability. The customary procedures of print media management, which are manualized in coordinating, fixed scheduling as well as fragmented data disclosures are progressively insufficient to the requirements. The given paper explores the implementation of intelligent workflow automation in the management of print media using the combination of artificial intelligence, machine learning, Internet of Things (IoT), and digital twin technologies. A perception-cognition-action-based paradigm is suggested to be the structured AI-driven workflow architecture to allow the adaptive data-driven orchestration between pre-press, printing, post-press, and distribution stages. Predictive quality control, dynamic scheduling, anomaly detection, and resource optimization are performed with the help of machine learning and decision-support models, whereas real-time physical-virtual synchronization and scenario-based planning is performed with the help of digital twins. The quantitative assessment indicates that there are great improvements in turnaround time, schedule compliance, machine use, quality rework, downtime, material waste, and energy usage. The findings reveal that intelligent workflow automation has multidimensional benefits of efficient operations, cost-saving, and sustainability, and maintains human controls by providing supervisory decision-support interfaces. The research paper finds that intelligent workflow automation is a strategic asset in the production workflow of contemporary print media organizations that aim to have resilient, efficient, and sustainable production systems.

The disappearance of modernist artworks through the historical conflict, material degradation and partiality of archival regimes poses a continuous problem in terms of art history and cultural heritage conservation. It is a foregone conclusion that such works are difficult to reconstruct, since modernist aesthetics is focused on abstraction, fragmentation, and conceptual purpose over representational devotion. In this paper, the author suggests a reconstruction model based on AI that views artistic reconstruction as a probabilistic inference problem, as opposed to a deterministic recovery problem. The system is already based on the framework of representation learning, conditional generative modeling, uncertainty-aware sampling, and human-in-the-loop validation to produce several possible discovery hypotheses based on the existing evidence and style samples. The hierarchical style modeling strategy gives reconstruction possibilities at artist and movements levels, whereas explicit uncertainty signs circumvent overconfidence in interpretation. The framework is tested by using representative case studies which are partially documented, partially fragmented and entirely lost modernist artworks. The quantitative metrics of consistency, the analysis of perceptual styles, and uncertainty analysis and the qualitative evaluation process performed by experts prove that the reconstruction fidelity is proportional to the strength of evidence and that the uncertainty is explicitly magnified in the case of sparse constraints. The findings indicate the significance of AI as a supportive analysis tool that can aid the judgment of curators and art-historians but does not sacrifice the interpretative responsibility.