Appropriate postoperative pain management contributes to earlier mobilization, shorter hospitalization, and reduced cost. The under treatment of pain may impede short-term recovery and have a detrimental long-term effect on health. This study focuses on Patient Controlled Analgesia (PCA), which is a delivery system for pain medication. This study proposes and demonstrates how to use machine learning and data mining techniques to predict analgesic requirements and PCA readjustment.

Graphene quantum dots (GQDs), nanomaterials derived from graphene and carbon dots, are highly stable, soluble, and have exceptional optical properties. Further, they have low toxicity and are excellent vehicles for carrying drugs or fluorescein dyes. Specific forms of GQDs can induce apoptosis and could be used to treat cancers. In this study, three forms of GQDs (GQD (nitrogen:carbon = 1:3), ortho-GQD, and meta-GQD) were screened and tested for their potential to inhibit breast cancer cell (MCF-7, BT-474, MDA-MB-231, and T-47D) growth. All three GQDs decreased cell viability after 72 h of treatment and specifically affected breast cancer cell proliferation. An assay for the expression of apoptotic proteins revealed that p21 and p27 were up-regulated (1.41-fold and 4.75-fold) after treatment. In particular, ortho-GQD-treated cells showed G2/M phase arrest. The GQDs specifically induced apoptosis in estrogen receptor-positive breast cancer cell lines. These results indicate that these GQDs induce apoptosis and G2/M cell cycle arrest in specific breast cancer subtypes and could potentially be used for treating breast cancers.

Background: Microcalcifications in the breast are often an early warning sign of breast cancer, and their accurate detection is crucial for the early discovery and management of the disease. In recent years, deep learning technology, particularly models based on object detection, has significantly improved the ability to detect microcalcifications. This study aims to use the advanced YOLO-v8 object detection algorithm to identify breast microcalcifications and explore its advantages in terms of performance and clinical application. Methods: This study collected mammograms from 7615 female participants, with a dataset including 10,323 breast images containing microcalcifications. We used the YOLO-v8 model for microcalcification detection and trained and validated the model using five-fold cross-validation. The model’s performance was evaluated through metrics such as accuracy, recall, F1 score, mAP50, and mAP50-95. Additionally, this study explored the potential applications of this technology in clinical practice. Results: The YOLO-v8 model achieved an mAP50 of 0.921, an mAP50-95 of 0.709, an F1 score of 0.82, a detection accuracy of 0.842, and a recall rate of 0.796 in breast microcalcification detection. Compared to previous similar deep learning object detection techniques like Mask R-CNN, YOLO-v8 has shown improvements in both speed and accuracy. Conclusions: YOLO-v8 outperforms traditional detection methods in detecting breast microcalcifications. Its multi-scale detection capability significantly enhances both speed and accuracy, making it more clinically practical for large-scale screenings. Future research should further explore the model’s potential in benign and malignant classification to promote its application in clinical settings, assisting radiologists in diagnosing breast cancer more efficiently.

Background: Even though there are continually upgraded recommendations for managing sepsis, such as "Surviving Sepsis Campaign: international guidelines for management of severe sepsis and septic shock", mortality is still high. Si-Ni-Tang, a remedy documented in Shanghan Lun, a medical collection from ancient China, is used for treating patients with sepsis and septic shock. Using a well-designed clinical trial, we are eager to survey the effectiveness of the concurrent use of this remedy in restoring these patients' hemodynamic status, or "Yang Qi". Methods/Design: Patients admitted to our medical intensive care units with the diagnosis of septic shock, defined as persistent hypotension induced by sepsis despite adequate fluid resuscitation, are eligible for participation. The inclusion criteria include: age from 20 to 85 years, conditions meeting the definition of septic shock, use of vasopressors within 24 hours of entering the study, and use of a nasogastric tube for feeding. The enrolled patients are randomly allocated either to the Si-Ni-Tang group or the placebo group. The prescription of the trial drugs (Si-Ni-Tang/placebo) is 2.25 grams 4 times a day for 7 days or till shock reversal (if shock reversal occurs in less than 7 days). Data, including duration of vasopressor infusion, gender, age, co-morbidities, APACHE II score, predicted mortality, ICU mortality, ICU length of stay, hospital mortality, hospital length of stay, source of sepsis, and culture results, are collected for the following analysis. Discussion: Si-Ni-Tang is composed of processed Zingiber officinale, Glycyrrhiza uralensis, and Aconitum carmichaeli. Zingiber officinale and Glycyrrhiza uralensis are found to have the ability to reduce pro-inflammatory cytokine production, to inhibit lipopolisaccharide-induced macrophage activation and function, and to lessen the bacterial load and suppress acute and chronic inflammation. Aconitum carmichaeli is known to have vasopressor activity, and positive chronotropic and inotropic effects. As this remedy has a potential benefit in treating septic shock patients, we designed a double-blind, prospective, randomized controlled trial and would like to publish the results and conclusions later. Trial Registration: ClinicalTrials.gov: NCT01223430

In the modern healthcare system, the rational allocation of emergency department (ED) resources is crucial for enhancing emergency response efficiency, ensuring patient safety, and improving the quality of medical services. This paper focuses on the issue of ED resource allocation and designs a priority sorting system for ED patients. The system classifies patients into two queues: urgent and routine. Considering different service rates, a multi-server preemptive priority queueing model \((M/M/c_{1}/K)\) and a multi-server non-preemptive priority queueing model \((M/M/c_{2}/\infty )\) are constructed. Additionally, the number of beds, K, is introduced as the capacity of the urgent queue. By comprehensively considering the costs associated with patient waiting time, the cost of rejecting the most critical patients, and the total costs of beds and servers, a mixed-integer programming model was constructed with the objective of minimizing the total cost. The particle swarm optimization algorithm was applied to determine the optimal number of servers, service rate, and number of beds. Compared with the model proposed by Alipour-Vaezi et al., our model significantly improves patient waiting times and queue lengths using the same data set: the waiting time \(W_{q}^{1}\) decreased by 74.44%, \(W_{q}^{3}\) by 5.79%, and \(W_{q}^{4}\) by 1.13%; the queue length \(L_{q}^{1}\) decreased by 78% and \(L_{q}^{3}\) by 3.33%. Our model effectively reduces patient waiting times and queue lengths while controlling costs, identifies the optimal number of beds, and achieves optimized resource allocation. Finally, we conducted a sensitivity analysis and provided some valuable management insights.

Traditional Chinese medicine (TCM) has relied on pulse diagnosis as a cornerstone of healthcare assessment for thousands of years. Despite its long history and widespread use, TCM pulse diagnosis has faced challenges in terms of diagnostic accuracy and consistency due to its dependence on subjective interpretation and theoretical analysis. This study introduces an approach to enhance the accuracy of TCM pulse diagnosis for diabetes by leveraging the power of deep learning algorithms, specifically LeNet and ResNet models, for pulse waveform analysis. LeNet and ResNet models were applied to analyze TCM pulse waveforms using a diverse dataset comprising both healthy individuals and patients with diabetes. The integration of these advanced algorithms with modern TCM pulse measurement instruments shows great promise in reducing practitioner-dependent variability and improving the reliability of diagnoses. This research bridges the gap between ancient wisdom and cutting-edge technology in healthcare. LeNet-F, incorporating special feature extraction of a pulse based on TMC, showed improved training and test accuracies (73% and 67%, respectively, compared with LeNet’s 70% and 65%). Moreover, ResNet models consistently outperformed LeNet, with ResNet18-F achieving the highest accuracy (82%) in training and 74% in testing. The advanced preprocessing techniques and additional features contribute significantly to ResNet18-F’s superior performance, indicating the importance of feature engineering strategies. Furthermore, the study identifies potential avenues for future research, including optimizing preprocessing techniques to handle pulse waveform variations and noise levels, integrating additional time–frequency domain features, developing domain-specific feature selection algorithms, and expanding the scope to other diseases. These advancements aim to refine traditional Chinese medicine pulse diagnosis, enhancing its accuracy and reliability while integrating it into modern technology for more effective healthcare approaches.