Karnataka Institute of Medical Sciences

The pace of Ayurveda industry growth with respect to herbal drug market, healthcare products and wellness products market is highly remarkable especially after COVID 19 pandemic. Medical tourism also sharing quiet a large amount in the AYUSH market growth. In order to compete with China at global level, there is urgent need of high-quality research and high-quality publications. The department of AYUSH has taken initiatives to establish global center for Traditional medicine (GCTM) at Jamnagar, SPARK, PG-Star, SMART program, AYUSH portal, AYURGENOMICS, Pharmacovigilance for ASU drugs and many more. JAHM wholeheartedly welcome these initiatives. But there is urgent need to expand our research infrastructure, research training center for producing skilled researcher to carry top quality research and generate high quality research data and evidences for globalizing the Ayurveda. The reasons for poor quality publications in ayurveda are – a. Decreased research methods and practical application among the ayurveda fraternity, b. No practical exposure for ayurveda fraternity to learn high quality research skills during PG, doctoral or post-doc level, c. Poor training of researchers, and d. Poor infrastructure. Most of the Ayurveda journals which are not indexed in SCOPUS or PUBMED, are not following any ethics, stringent peer review, reporting standards and guidelines; simply for earning money they are publishing the poor-quality articles. The govt and the related department are acting like mute spectator, not taking any action. Finally, the larger question remains—whom should we blame? Instead of blaming each other, it is time for collective responsibility. I sincerely urge entire Ayurveda fraternity to engage more deeply in meaningful research and high-quality scientific publishing. Let us train our students in rigorous research methodology and ethical scholarly writing. Together, we must take a pledge to produce top-class research and high-quality publications that strengthen the global credibility and scientific foundation of Ayurveda.

Mandibular third molar (MTM) extractions are among the most frequent oral surgical procedures, often associated with variable surgical difficulty and postoperative complications. Artificial intelligence (AI) offers potential in predicting these outcomes to enhance preoperative planning. This study was done to evaluate the utility of AI in predicting surgical difficulty and postoperative morbidity in MTM extractions using cone-beam computed tomography (CBCT) data and patient variables. A cross-sectional study of 40 patients undergoing MTM extraction was conducted. AI algorithms (random forest and convolutional neural networks) analyzed CBCT features and clinical parameters to predict difficulty and morbidity. Statistical evaluation was done using SPSS version 26. The AI model demonstrated an overall accuracy of 87.5% in predicting surgical difficulty and 82.3% for postoperative morbidity. Significant predictors included root morphology, proximity to the inferior alveolar nerve, and patient age (P < 0.05). AI-based models can effectively predict MTM extraction difficulty and postoperative morbidity, facilitating personalized treatment planning.

Tonsillectomy is one of the most commonly performed surgical procedures on children. Indications for tonsillectomy include recurrent tonsillitis, obstructive sleep apnea, and peritonsillar abscess, among others. Bipolar diathermy and coblation surgeries are the most common used surgeries. Bipolar Diathermy efficiently manages postoperative hemorrhage but often results in increased postoperative pain due to higher thermal tissue destruction. In contrast, coblation is a procedure that offers a lower output of heat and results in lesser pain and shorter healing times. The present study compares the above two procedures to evaluate the postoperative outcomes regarding pain, healing, and safety of pediatric tonsillectomy patients. This was a prospective, randomized, experimental study on 120 pediatric patients ranging from 5 to 14 years who experienced recurrent or chronic tonsillitis or obstructive symptoms, amongst them sleep apnea. Both groups were operated on with extracapsular tonsillectomy under general anaesthesia, with standardized preoperative management and uniform anaesthetic protocols. The aforementioned parameters of pain, hemorrhage, and healing process were similarly followed up for postoperative evaluations at one, two, three, and 4 weeks. Mean scores of pain were very highly significantly lower in patients of Group A at visits than in patients of Group B. Patients of Group A received scores for visit for the first week at 9.45 ± 0.58 versus 9.82 ± 0.22 (p = 0.001) and scores for visit in the fourth week at 0.35 ± 0.40 versus 1.92 ± 1.78 (p = 0.001). Group A compared with group B had early healing time and fewer cases of bleeding complications. Time to pain resolution was shorter in Group A with a mean of 15.5 ± 2.0 days compared with a mean of 24.8 ± 3.4 days in Group B, (p = 0.001). The study results show that in pediatric patients, coblation-assisted tonsillectomy proved to have advantageous benefits over bipolar diathermy tonsillectomy. Coblation provided less postoperative pain and quick healing, with an overall shorter pain duration and fewer bleeding complications.

Budget allocation for the AYUSH ministry seen 20 per cent rise, from Rs 3,671.82 crore in FY 2025-26 to Rs 4,408.93 crore in FY 2026-27, prioritizing the evidence-based research, infrastructure expansion, and standardization of traditional medicine. Key steps include establishing three new All India Institutes of Ayurveda to expand research. Among the Centrally sponsored schemes, the allocation for the National AYUSH Mission has been raised from Rs 780.96 crore in FY 2025-26 to Rs 1,300 crore in FY 2026-27, marking 66.5 per cent hike. The AYUSH budget is equal to the budget of the Indian Council of Medical Research (ICMR). The current ICMR budget is 27% increase compared to the previous year. This shows the discrimination and injustice to AYUSH, as the budget allocated is very small. The allocation for the All India Institute of Ayurveda has been decreased by 12 per cent. Funding for the Institute of Teaching and Research in Ayurveda is declined from 279.24 crore to 219.05 crore. Its high time for all the stakeholders of Ayurveda to force the PMO and Finance ministry to increase the funds for AYUSH ministry. At the same time it’s the need of hour for AYUSH to frame new research policy and create infrastructure for conducting high quality research in India and abroad.

Background: Malaria remains a significant public health problem in India, particularly in endemic states such as Karnataka. Accurate forecasting of malaria incidence is essential for strengthening surveillance systems, optimizing resource allocation and enabling timely preventive interventions. Objective: This study aimed to analyze the temporal pattern of malaria cases in Karnataka State, India and to develop an appropriate Seasonal Autoregressive Integrated Moving Average (SARIMA) model for forecasting future monthly malaria incidence. Methods: A retrospective time series analysis was conducted using monthly malaria case data from January 2020 to August 2025 obtained from the National Centre for Vector Borne Diseases Control (NCVBDC), Government of India. The Box–Jenkins methodology was applied for model development. Data preprocessing included logarithmic transformation and seasonal and non-seasonal differencing to achieve stationary. Model identification was guided by autocorrelation function (ACF) and partial autocorrelation function (PACF) plots. Competing SARIMA models were estimated using maximum likelihood estimation and evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Bayesian Information Criterion (BIC). Residual diagnostics were assessed using the Ljung–Box Q test. Results: The malaria time series demonstrated a clear increasing trend with pronounced seasonal variation. Among the candidate models, the SARIMA (2,1,0) (0,1,1) ₁₂ model showed the best overall performance, with the lowest RMSE (29.413), MAE (18.279), MAPE (40.437) and normalized BIC (7.054). The Ljung–Box Q test indicated no residual autocorrelation (p = 0.77). Twelve-month forecasts (September 2025–August 2026) revealed marked seasonal peaks, with forecasted values ranging from 43 to 461 cases. Conclusion: The SARIMA (2,1,0) (0,1,1) ₁₂ model effectively captured the trend and seasonal dynamics of malaria transmission in Karnataka. SARIMA based forecasting offers a practical and reliable tool for malaria surveillance and can support evidence-based planning and timely public health interventions. Keywords: Malaria, Time series analysis, SARIMA, Forecasting, Karnataka, Public health