PURPOSE OF REVIEW: Although many therapeutic approaches have been lined up nowadays to treat Diabetes, there are no proper treatment modalities proposed yet in treating diabetic wounds due to the lack of understanding about the role of inflammatory mediators, especially Pro-inflammatory mediators- Cytokines, in the process of Wound healing which we mainly focus on this review.
RECENT FINDINGS: Although complications of Diabetes mellitus are most reported after years of diagnosis, the most severe critical complication is impaired Wound Healing among Diabetes patients. Even though Trauma, Peripheral Artery Disease, and Peripheral Neuropathy are the leading triggering factors for the development of ulcerations, the most significant issue contributing to the development of complicated cutaneous wounds is wound healing impairment. It may even end up with amputation. Newer therapeutic approaches such as incorporating the additives in the present dressing materials, which include antimicrobial molecules and immunomodulatory cytokines is of better therapeutic value.
SUMMARY: The adoption of these technologies and the establishment of novel therapeutic interventions is difficult since there is a gap in terms of a complete understanding of the pathophysiological mechanisms at the cellular and molecular level and the lack of data in terms of the assessment of safety and bioavailability differences in the individuals' patients. The target-specific pro-inflammatory cytokines-based therapies, either by upregulation or downregulation of them, will be helpful in the wound healing process and thereby enhances the Quality of life in patients, which is the goal of drug therapy.
RESULTS: The oocyte AI model demonstrated area under the curve (AUC) up to 0.65 on two blind test datasets. High sensitivity for predicting competent oocytes (83-88%) was offset by lower specificity (26-36%). Exclusion of confounding biological variables (male factor infertility and maternal age ≥35 years) improved AUC up to 14%, primarily due to increased specificity. AI score correlated with size of the zona pellucida and perivitelline space, and ooplasm appearance. AI score also correlated with blastocyst expansion grade and morphological quality. The sum of AI scores from oocytes in group culture images predicted the formation of two or more usable blastocysts (AUC 0.77).
CONCLUSION: An AI model to evaluate oocyte competence was developed using federated learning, representing an essential step in protecting patient data. The AI model was significantly predictive of oocyte competence, as defined by usable blastocyst formation, which is a critical factor for IVF success. Potential clinical utility ranges from selective oocyte fertilization to guiding treatment decisions regarding additional rounds of oocyte retrieval.
DESIGN: In total, 10,677 oocyte images with associated metadata were collected prospectively by eight IVF clinics across six countries. AI training used federated learning, where data were retained on regional servers to comply with data privacy laws. The final AI model required a single image as input to evaluate oocyte competence, which was defined by the formation of a usable blastocyst (≥expansion grade 3 by day 5 or 6 post ICSI).
METHOD: By using visual recognition technology, motion capture technology, and advanced multimodal large language models with a comprehensive professional table tennis knowledge base, the system accurately identifies common errors made by beginners and provides targeted training guidance.
RESULT: The AI Table Tennis Coaching System demonstrates high accuracy in identifying mistakes made by beginner players, particularly in recognizing arm-related errors and racket-related errors, with accuracies reaching 73% and 82% respectively.
CONCLUSION: The system operates at low costs, is easy to deploy, and offers a high cost-performance ratio, providing effective technological support for table tennis teaching and training. The AI table tennis coaching system is expected to play a significant role in enhancing training efficiency, promoting athlete skill improvement, and popularizing the sport. Future research will focus on improving the accuracy of footwork recognition in AI table tennis coaching systems and expanding their capability to provide training guidance for high-level athletes, thereby promoting the overall advancement of table tennis.