ASHTVIDHA SHASTRA KARMA WITH SUTURING IN DETAIL

Ayurveda is considered by many scholars to be the oldest healing science. In sanskrit, Ayurveda means the Science of life. Ayurvedic knowledge originated in India more than 5000 years ago and is often called the Mother of healing. Ancient surgical science- Shalya Tantra is one of the vital components of Ayurveda science which involves surgical and para-surgical interventions. The Shalya chikitsa deals with different surgical approaches for the management of various diseases such as Bhagandra, Pilonidal sinus, Arshas etc. Shalya Tantra embraces all processes aiming at the removal of factors responsible for producing pain or misery to the body or mind. Acharya sushruta has mentioned Trividha Karma and Ashtavidha Shastra Karma as versatile approaches for therapeutic purposes. The concept of Ashtavidha karma is a unique contribution of Acharya Sushruta. These eight specific surgical procedures are useful in the management of all the diseases which require surgical intervention. In the present time though modern surgery has developed a lot but the basic procedures used in major conditions remained same. These eight basic surgical procedures mentioned by Sushruta are equally applied even today with required modifications to manage the diseases which require surgery including surgical emergency conditions. This article summarizes role of Ashtvidha Shastra Karma in Shalya karma for the management of various surgical problems

TRIVIDHA KARMA IN SURGICAL PARLANCE- A CONCEPTUAL STUDY

In Ayurvedic classics there are various types of treatment and Shastra chikitsa is one among them. Purva Karma Pradhana Karma, Paschat Karma are Trividha Karma. According to Acharya Sushruta, Purva Karma means preparation of patient along with collecting all the materials needed during the Pradhana karma. Ashtaviddha Shastra Karma is included in Pradhana Karma. Paschat Karma includes post operative care. Sushruta division of surgical activity into three parts i.e., pre- operative, operative and post-operative based on sound scientific principles. Sushruta has also described the pre-operative appreciation of foreign body, its size, shape, and exact location within the body and appropriate instrument for its removal should be selected pre-operatively. He has also mentioned the pre-operative diet and starvation for various types of surgeries. He has also emphasized that asepsis and antisepsis precaution should be taken, wound should be protected from dangerous and invisible creatures (Nishachara). Fumigation of Vranitaagara should be done for ten days, twice a day. By virtue of this article, we can understand the systematic method of arranging the surgical experience of arranging the surgical experience of the older surgeon, about preliminary measures, principal measures and after measures. All the procedures included under these three headings i.e., Trividdha karma plays an important role in successful and complication free surgery

Coronavirus (COVID-19): ARIMA based time-series analysis to forecast near future

COVID-19, a novel coronavirus, is currently a major worldwide threat. It has infected more than a million people globally leading to hundred-thousands of deaths. In such grave circumstances, it is very important to predict the future infected cases to support prevention of the disease and aid in the healthcare service preparation. Following that notion, we have developed a model and then employed it for forecasting future COVID-19 cases in India. The study indicates an ascending trend for the cases in the coming days. A time series analysis also presents an exponential increase in the number of cases. It is supposed that the present prediction models will assist the government and medical personnel to be prepared for the upcoming conditions and have more readiness in healthcare systems.Comment: Pages: 11, Tables: 4, Figures: 6; Author Contributions: H.T. and T.C. conceptualized the project. H.T. designed the study, performed the computations and investigations, contributed to data analysis and wrote the manuscript. P.R. provided the resources. T.C. and V.S. supervised the study and reviewed the manuscrip

Waste Management in Third World Countries

The objective of Project Compress is to outsource waste management in high density communities to support underdeveloped infrastructure in rising populations, thus targeting large populations.By allowing trash to be efficiently dealt with in individual homes, we hope to improve waste management and sanitation in underdeveloped areas with high population densities. Utilizing the effectiveness of a human-powered trash compactor, local communities will pre-process trash which will ease the burden of trash logistics and processing. Providing trash compactors to households will incentivize proper waste management techniques which will decrease pollution while increasing sanitation. Proper trash compaction will liberate property that may have been polluted which allows land to be utilized for commercial and residential purposes, therefore boosting the economy of the environment. This project will follow a transaction revenue stream, with particular focus on asset sale. It is a one-time investment that maintains a high automation for self sustainment. Project Compress is a unique implementation of sustainable trash compression technology that will revolutionize how countries approach infrastructure innovations. The compactor will be distributed with the assistance of the Indian Government and WasteAid, along with local plastic manufacturers to create jobs within the target economy

Language Models can be Logical Solvers

Logical reasoning is a fundamental aspect of human intelligence and a key component of tasks like problem-solving and decision-making. Recent advancements have enabled Large Language Models (LLMs) to potentially exhibit reasoning capabilities, but complex logical reasoning remains a challenge. The state-of-the-art, solver-augmented language models, use LLMs to parse natural language logical questions into symbolic representations first and then adopt external logical solvers to take in the symbolic representations and output the answers. Despite their impressive performance, any parsing errors will inevitably result in the failure of the execution of the external logical solver and no answer to the logical questions. In this paper, we introduce LoGiPT, a novel language model that directly emulates the reasoning processes of logical solvers and bypasses the parsing errors by learning to strict adherence to solver syntax and grammar. LoGiPT is fine-tuned on a newly constructed instruction-tuning dataset derived from revealing and refining the invisible reasoning process of deductive solvers. Experimental results on two public deductive reasoning datasets demonstrate that LoGiPT outperforms state-of-the-art solver-augmented LMs and few-shot prompting methods on competitive LLMs like ChatGPT or GPT-4.Comment: Preprin

Fine-Tuning Language Models with Advantage-Induced Policy Alignment

Reinforcement learning from human feedback (RLHF) has emerged as a reliable approach to aligning large language models (LLMs) to human preferences. Among the plethora of RLHF techniques, proximal policy optimization (PPO) is of the most widely used methods. Despite its popularity, however, PPO may suffer from mode collapse, instability, and poor sample efficiency. We show that these issues can be alleviated by a novel algorithm that we refer to as Advantage-Induced Policy Alignment (APA), which leverages a squared error loss function based on the estimated advantages. We demonstrate empirically that APA consistently outperforms PPO in language tasks by a large margin, when a separate reward model is employed as the evaluator. In addition, compared with PPO, APA offers a more stable form of control over the deviation from the model's initial policy, ensuring that the model improves its performance without collapsing to deterministic output. In addition to empirical results, we also provide a theoretical justification supporting the design of our loss function

Determining the Physiological Effects of Opioid Addiction through the Application of Spared Nerve Injury Model of Neuropathic Pain on the Morphine Self-Administration Rodent Model

The aim of this project is to determine whether morphine reinforcement and seeking behavior in enhanced in SNI mice trained to self-administer morphine. Testing was completed by examining behavioral approaches of both SNI and sham lesioned mice, with a focus in MSA. Mice were tested 2 months after catheter implantation, with active doses of 0.1/mg/kg/infusion. An FR1, 13 days training procedure was used, with each level press triggering illumination of the cue light for 6 seconds and the in-house light for 20 seconds to indicate a timeout period. To determine the reinforcing capacity of the morphine, a progressive ratio schedule was used to quantify seeking behavior extinction and reinstatement through drug-primed and pain-induced reinstatement, which showed a clear distinction between SNI and Sham addiction patterns. These results can be used to guide chemogenetic manipulation of key nodes in the VTA-NAc circuitry in an attempt to reverse SNI-induced changes in drug seeking behavior; particularly regarding the VTA DA neurons that intersection the shell and core as well as investigating if the MSA alters the SNI induced adaptations