The PATCH trial: 5% lidocaine-medicated plaster for trigeminal neuralgia-Results of a multicentric, enriched enrollment, randomized withdrawal, double-blind, vehicle-controlled, parallel-group study.

OBJECTIVE: To explore the efficacy and safety of 5% lidocaine-medicated plaster (LMP) in patients with trigeminal neuralgia (TN). BACKGROUND: TN is an excruciatingly painful type of neuropathic facial pain. Anti-epileptics are the first-line treatment for TN; however, these oral drugs alone sometimes fail to achieve satisfactory analgesic effects. Two retrospective studies have shown that LMP can be an effective and safe treatment option for some patients with TN. No other high-quality clinical studies have explored the effect and safety of LMP in patients with TN. METHODS: The PATCH trial is an enriched enrollment with randomized withdrawal, double-blind, vehicle-controlled, parallel-group trial performed at five study centers. Eligible patients with TN received LMP during a 3-week initial open-label phase. Patients who met the response criteria entered the double-blind treatment phase and were randomly assigned for treatment with either LMP (LMP group) or vehicle patches (control group) at a 1:1 ratio. Patients who met the criteria for treatment failure were withdrawn from the double-blind treatment phase, and treatment was continued in the remaining patients for up to 28 days. The primary outcome was the number of treatment failures. The secondary endpoints were the time to loss of therapeutic response (LTR) in the double-blind phase and the weekly mean pain severity in both the open-label phase and the double-blind phase of the study. RESULTS: The first patient was enrolled in this study on May 1, 2021, and the enrollment of the last patient was completed on August 26, 2022. A total of 307 patients were initially screened, 226 (74.0%) of whom entered the open-label phase. Of the 226 respondents, 124 (55.0%) were randomized to the double-blind phase. In the double-blind phase, 62 patients were assigned to the LMP group, and 62 were assigned to the control group. For the primary endpoint, 16 (26.0%) patients with LMP and 36 (58.0%) patients with vehicle patches met the treatment failure criteria during the double-blind phase (relative risk, 0.48; 95% confidence interval [CI], 0.31 to 0.75; p < 0.001). The survival curve of the LTR showed that the LTR of LMP was significantly longer than that of the vehicle patches (hazard ratio, 0.275; 95% CI, 0.15 to 0.50; log-rank p < 0.001). LMP also significantly reduced the weekly mean pain severity in the double-blind phase of the study (p = 0.007). CONCLUSIONS: LMP produced partial relief of pain symptoms in some patients with TN. For responders, LMP may be used as an add-on therapy in a multidrug treatment protocol.

Detection of Chrysanthemums Inflorescence Based on Improved CR-YOLOv5s Algorithm.

Accurate recognition of the flowering stage is a prerequisite for flower yield estimation. In order to improve the recognition accuracy based on the complex image background, such as flowers partially covered by leaves and flowers with insignificant differences in various fluorescence, this paper proposed an improved CR-YOLOv5s to recognize flower buds and blooms for chrysanthemums by emphasizing feature representation through an attention mechanism. The coordinate attention mechanism module has been introduced to the backbone of the YOLOv5s so that the network can pay more attention to chrysanthemum flowers, thereby improving detection accuracy and robustness. Specifically, we replaced the convolution blocks in the backbone network of YOLOv5s with the convolution blocks from the RepVGG block structure to improve the feature representation ability of YOLOv5s through a multi-branch structure, further improving the accuracy and robustness of detection. The results showed that the average accuracy of the improved CR-YOLOv5s was as high as 93.9%, which is 4.5% better than that of normal YOLOv5s. This research provides the basis for the automatic picking and grading of flowers, as well as a decision-making basis for estimating flower yield.

Pine wilt disease detection algorithm based on improved YOLOv5.

Pine wilt disease (PWD) poses a significant threat to forests due to its high infectivity and lethality. The absence of an effective treatment underscores the importance of timely detection and isolation of infected trees for effective prevention and control. While deep learning techniques combined unmanned aerial vehicle (UAV) remote sensing images offer promise for accurate identification of diseased pine trees in their natural environments, they often demand extensive prior professional knowledge and struggle with efficiency. This paper proposes a detection model YOLOv5L-s-SimAM-ASFF, which achieves remarkable precision, maintains a lightweight structure, and facilitates real-time detection of diseased pine trees in UAV RGB images under natural conditions. This is achieved through the integration of the ShuffleNetV2 network, a simple parameter-free attention module known as SimAM, and adaptively spatial feature fusion (ASFF). The model boasts a mean average precision (mAP) of 95.64% and a recall rate of 91.28% in detecting pine wilt diseased trees, while operating at an impressive 95.70 frames per second (FPS). Furthermore, it significantly reduces model size and parameter count compared to the original YOLOv5-Lite. These findings indicate that the proposed model YOLOv5L-s-SimAM-ASFF is most suitable for real-time, high-accuracy, and lightweight detection of PWD-infected trees. This capability is crucial for precise localization and quantification of infected trees, thereby providing valuable guidance for effective management and eradication efforts.

Deep Learning Combined with Radiologist's Intervention Achieves Accurate Segmentation of Hepatocellular Carcinoma in Dual-Phase Magnetic Resonance Images.

Purpose: Segmentation of hepatocellular carcinoma (HCC) is crucial; however, manual segmentation is subjective and time-consuming. Accurate and automatic lesion contouring for HCC is desirable in clinical practice. In response to this need, our study introduced a segmentation approach for HCC combining deep convolutional neural networks (DCNNs) and radiologist intervention in magnetic resonance imaging (MRI). We sought to design a segmentation method with a deep learning method that automatically segments using manual location information for moderately experienced radiologists. In addition, we verified the viability of this method to assist radiologists in accurate and fast lesion segmentation. Method: In our study, we developed a semiautomatic approach for segmenting HCC using DCNN in conjunction with radiologist intervention in dual-phase gadolinium-ethoxybenzyl-diethylenetriamine penta-acetic acid- (Gd-EOB-DTPA-) enhanced MRI. We developed a DCNN and deep fusion network (DFN) trained on full-size images, namely, DCNN-F and DFN-F. Furthermore, DFN was applied to the image blocks containing tumor lesions that were roughly contoured by a radiologist with 10 years of experience in abdominal MRI, and this method was named DFN-R. Another radiologist with five years of experience (moderate experience) performed tumor lesion contouring for comparison with our proposed methods. The ground truth image was contoured by an experienced radiologist and reviewed by an independent experienced radiologist. Results: The mean DSC of DCNN-F, DFN-F, and DFN-R was 0.69 ± 0.20 (median, 0.72), 0.74 ± 0.21 (median, 0.77), and 0.83 ± 0.13 (median, 0.88), respectively. The mean DSC of the segmentation by the radiologist with moderate experience was 0.79 ± 0.11 (median, 0.83), which was lower than the performance of DFN-R. Conclusions: Deep learning using dual-phase MRI shows great potential for HCC lesion segmentation. The radiologist-aided semiautomated method (DFN-R) achieved improved performance compared to manual contouring by the radiologist with moderate experience, although the difference was not statistically significant.

TBC-YOLOv7: a refined YOLOv7-based algorithm for tea bud grading detection.

Introduction: Accurate grading identification of tea buds is a prerequisite for automated tea-picking based on machine vision system. However, current target detection algorithms face challenges in detecting tea bud grades in complex backgrounds. In this paper, an improved YOLOv7 tea bud grading detection algorithm TBC-YOLOv7 is proposed. Methods: The TBC-YOLOv7 algorithm incorporates the transformer architecture design in the natural language processing field, integrating the transformer module based on the contextual information in the feature map into the YOLOv7 algorithm, thereby facilitating self-attention learning and enhancing the connection of global feature information. To fuse feature information at different scales, the TBC-YOLOv7 algorithm employs a bidirectional feature pyramid network. In addition, coordinate attention is embedded into the critical positions of the network to suppress useless background details while paying more attention to the prominent features of tea buds. The SIOU loss function is applied as the bounding box loss function to improve the convergence speed of the network. Result: The results of the experiments indicate that the TBC-YOLOv7 is effective in all grades of samples in the test set. Specifically, the model achieves a precision of 88.2% and 86.9%, with corresponding recall of 81% and 75.9%. The mean average precision of the model reaches 87.5%, 3.4% higher than the original YOLOv7, with average precision values of up to 90% for one bud with one leaf. Furthermore, the F1 score reaches 0.83. The model's performance outperforms the YOLOv7 model in terms of the number of parameters. Finally, the results of the model detection exhibit a high degree of correlation with the actual manual annotation results ( R2 =0.89), with the root mean square error of 1.54. Discussion: The TBC-YOLOv7 model proposed in this paper exhibits superior performance in vision recognition, indicating that the improved YOLOv7 model fused with transformer-style module can achieve higher grading accuracy on densely growing tea buds, thereby enables the grade detection of tea buds in practical scenarios, providing solution and technical support for automated collection of tea buds and the judging of grades.

Automatic Brain Midline Surface Delineation on 3D CT Images With Intracranial Hemorrhage.

Brain midline delineation plays an important role in guiding intracranial hemorrhage surgery, which still remains a challenging task since hemorrhage shifts the normal brain configuration. Most previous studies detected brain midline on 2D plane and did not handle hemorrhage cases well. We propose a novel and efficient hemisphere-segmentation framework (HSF) for 3D brain midline surface delineation. Specifically, we formulate the brain midline delineation as a 3D hemisphere segmentation task, and employ an edge detector and a smooth regularization loss to generate the midline surface. We also introduce a distance-weighted map to keep the attention on the midline. Furthermore, we adopt rectification learning to handle various head poses. Finally, considering the complex situation of ventricle break-in for hemorrhages in bilateral intraventricular (B-IVH) cases, we identify those cases via a classification model and design a midline correction strategy to locally adjust the midline. To our best knowledge, it is the first study focusing on delineating the brain midline surface on 3D CT images of hemorrhage patients and handling the situation of ventricle break-in. Extensive validation on our large in-house datasets (519 patients) and the public CQ500 dataset (491 patients), demonstrates that our method outperforms state-of-the-art methods on brain midline delineation.

Deep Segmentation Feature-Based Radiomics Improves Recurrence Prediction of Hepatocellular Carcinoma.

Objective and Impact Statement. This study developed and validated a deep semantic segmentation feature-based radiomics (DSFR) model based on preoperative contrast-enhanced computed tomography (CECT) combined with clinical information to predict early recurrence (ER) of single hepatocellular carcinoma (HCC) after curative resection. ER prediction is of great significance to the therapeutic decision-making and surveillance strategy of HCC. Introduction. ER prediction is important for HCC. However, it cannot currently be adequately determined. Methods. Totally, 208 patients with single HCC after curative resection were retrospectively recruited into a model-development cohort (n=180) and an independent validation cohort (n=28). DSFR models based on different CT phases were developed. The optimal DSFR model was incorporated with clinical information to establish a DSFR-C model. An integrated nomogram based on the Cox regression was established. The DSFR signature was used to stratify high- and low-risk ER groups. Results. A portal phase-based DSFR model was selected as the optimal model (area under receiver operating characteristic curve (AUC): development cohort, 0.740; validation cohort, 0.717). The DSFR-C model achieved AUCs of 0.782 and 0.744 in the development and validation cohorts, respectively. In the development and validation cohorts, the integrated nomogram achieved C-index of 0.748 and 0.741 and time-dependent AUCs of 0.823 and 0.822, respectively, for recurrence-free survival (RFS) prediction. The RFS difference between the risk groups was statistically significant (P<0.0001 and P=0.045 in the development and validation cohorts, respectively). Conclusion. CECT-based DSFR can predict ER in single HCC after curative resection, and its combination with clinical information further improved the performance for ER prediction.

Expert consensus of Chinese Association for the Study of Pain on the application of ozone therapy in pain medicine.

This consensus was compiled by first-line clinical experts in the field of pain medicine and was organized by the Chinese Association for the Study of Pain. To reach this consensus, we consulted a wide range of opinions and conducted in-depth discussions on the mechanism, indications, contraindications, operational specifications and adverse reactions of ozone iatrotechnique in the treatment of pain disorders. We also referred to related previous preclinical and clinical studies published in recent years worldwide. The purpose of this consensus is to standardize the rational application of ozone iatrotechnique in pain treatment, to improve its efficacy and safety and to reduce and prevent adverse reactions and complications in this process.

Expert consensus of the Chinese Association for the Study of Pain on ion channel drugs for neuropathic pain.

Neuropathic pain (NPP) is a kind of pain caused by disease or damage impacting the somatosensory system. Ion channel drugs are the main treatment for NPP; however, their irregular usage leads to unsatisfactory pain relief. To regulate the treatment of NPP with ion channel drugs in clinical practice, the Chinese Association for the Study of Pain organized first-line pain management experts from China to write an expert consensus as the reference for the use of ion channels drugs . Here, we reviewed the mechanism and characteristics of sodium and calcium channel drugs, and developed recommendations for the therapeutic principles and clinical practice for carbamazepine, oxcarbazepine, lidocaine, bulleyaconitine A, pregabalin, and gabapentin. We hope this guideline provides guidance to clinicians and patients on the use of ion channel drugs for the management of NPP.

Expert consensus of Chinese Association for the Study of Pain on the radiofrequency therapy technology in the Department of Pain.

On the basis of continuous improvement in recent years, radiofrequency therapy technology has been widely developed, and has become an effective method for the treatment of various intractable pain. Radiofrequency therapy is a technique that uses special equipment and puncture needles to output ultra-high frequency radio waves and accurately act on local tissues. In order to standardize the application of radiofrequency technology in the treatment of painful diseases, Chinese Association for the Study of Pain (CASP) has developed a consensus proposed by many domestic experts and scholars.

Multicentre, prospective, randomised, controlled, blinded-endpoint study to evaluate the efficacy and safety of pterygopalatine ganglion pulsed radiofrequency treatment for cluster headache: study protocol.

INTRODUCTION: Single-centre reports on small groups of patients have shown that pterygopalatine ganglion pulsed radiofrequency treatment in patients with refractory cluster headache (CH) can quickly relieve pain without significant side effects. However, a randomised controlled trial is still necessary to evaluate whether pterygopalatine ganglion pulsed radiofrequency (PRF) treatment is a viable treatment option for patients with CH who are not responding to drug treatment. METHODS AND ANALYSIS: This investigation is a multicentre, prospective, randomised, controlled, blinded-endpoint study. We will enrol 80 patients with CH who are not responding to medication. The enrolled patients will be randomly divided into two groups: the nerve block (NB) group and the PRF group. All patients will undergo CT-guided pterygopalatine ganglion puncture. A mixture containing steroids and local anaesthetics will be slowly injected into the patients in the NB group. The patients in the PRF group will be treated with PRF at 42°C for 360 s. After treatment, the duration of cluster periods; degree of pain during headache attacks; frequency of headache attacks; duration of each headache attack; dose of auxiliary analgesic drugs; duration of remission; degree of patient satisfaction; effectiveness rates at 1 day, 3 days, 1 week, 2 weeks, 1 month, 3 months, 6 months, and 1 year after the procedure; and intraoperative and postoperative adverse events will be compared between the two groups. ETHICS AND DISSEMINATION: This study was approved by the institutional ethics committee of the Beijing Tiantan Hospital (approval number: KY 2018-027-02). The results of the study will be published in peer-reviewed journals, and the findings will be presented at scientific meetings. TRIAL REGISTRATION NUMBER: NCT03567590; Pre-results.

Linguistic adaptation, validation and comparison of 3 routinely used neuropathic pain questionnaires.

BACKGROUND: Neuropathic pain questionnaires are efficient diagnostic tools for neuropathic pain and play an important role in neuropathic pain epidemiologic studies in China. No comparison data was available in regards to the Leeds Assessment of Neuropathic Symptoms and Signs (LANSS), the Neuropathic Pain Questionnaire (NPQ) and ID Pain within and among the same population. OBJECTIVE: To achieve a linguistic adaptation, validation, and comparison of Chinese versions of the 3 neuropathic pain questionnaires (LANSS, NPQ and ID Pain). STUDY DESIGN: A nonrandomized, controlled, prospective, multicenter trial. SETTING: Ten pain centers in China. METHODS: Two forward translations followed by comparison and reconciliation of the translations. Comparison of the 2 backward translations with the original version was made to establish consistency and accuracy of the translations. Pilot testing and pain specialists' evaluations were also required. A total of 140 patients were enrolled in 10 centers throughout China: 70 neuropathic pain patients and 70 nociceptive pain patients. Reliability (Cronbach's alpha coefficients and Guttman split-half coefficients) and validity (sensitivity, specificity, positive and negative predictive values, receiver operating characteristic [ROC] curves and the area under the ROC curves) of the 3 questionnaires were determined. ROC curves and the area under the ROC curves of the 3 questionnaires were also compared. RESULTS: Chinese versions of LANSS, NPQ and ID Pain had a good reliability (Cronbach's alpha coefficients and Guttman split-half coefficients were greater than 0.7). Sensitivity, specificity, positive and negative predictive values of the Chinese versions of LANSS and ID Pain were considerably high ( > 80%). The area under the ROC curves of LANSS and ID Pain was significantly higher than that of NPQ (P < 0.05). There was no statistically significant difference between the area under the ROC curves of LANSS and ID Pain (P > 0.05). LIMITATION: The study was based on patients with a high school degree or above, which limited the application of the 3 neuropathic pain questionnaires to patients with lower educational levels. CONCLUSION: The Chinese versions of LANSS and ID Pain developed and validated by this study can be used as a diagnostic tool in differentiating neuropathic pain in patients whose native language is Chinese (Mandarin).