A predictive model for L-T4 dose in postoperative DTC after RAI therapy and its clinical validation in two institutions.

Purpose: To develop a predictive model using machine learning for levothyroxine (L-T4) dose selection in patients with differentiated thyroid cancer (DTC) after resection and radioactive iodine (RAI) therapy and to prospectively validate the accuracy of the model in two institutions. Methods: A total of 266 DTC patients who received RAI therapy after thyroidectomy and achieved target thyroid stimulating hormone (TSH) level were included in this retrospective study. Sixteen clinical and biochemical characteristics that could potentially influence the L-T4 dose were collected; Significant features correlated with L-T4 dose were selected using machine learning random forest method, and a total of eight regression models were established to assess their performance in prediction of L-T4 dose after RAI therapy; The optimal model was validated through a two-center prospective study (n=263). Results: Six significant clinical and biochemical features were selected, including body surface area (BSA), weight, hemoglobin (HB), height, body mass index (BMI), and age. Cross-validation showed that the support vector regression (SVR) model was with the highest accuracy (53.4%) for prediction of L-T4 dose among the established eight models. In the two-center prospective validation study, a total of 263 patients were included. The TSH targeting rate based on constructed SVR model were dramatically higher than that based on empirical administration (Rate 1 (first rate): 52.09% (137/263) vs 10.53% (28/266); Rate 2 (cumulative rate): 85.55% (225/263) vs 53.38% (142/266)). Furthermore, the model significantly shortens the time (days) to achieve target TSH level (62.61 ± 58.78 vs 115.50 ± 71.40). Conclusions: The constructed SVR model can effectively predict the L-T4 dose for postoperative DTC after RAI therapy, thus shortening the time to achieve TSH target level and improving the quality of life for DTC patients.

Long-Term Efficacy and Safety of Stapokibart in Adults with Moderate-to-Severe Atopic Dermatitis: An Open-Label Extension, Nonrandomized Clinical Trial.

BACKGROUND: Stapokibart/CM310, a humanized monoclonal antibody targeting the interleukin-4 receptor α chain, has shown promising treatment benefits in patients with moderate-to-severe atopic dermatitis in previous phase II clinical trials. OBJECTIVE: We aimed to evaluate the long-term efficacy and safety of stapokibart in adults with moderate-to-severe atopic dermatitis. METHODS: Enrolled patients who previously completed parent trials of stapokibart received a subcutaneous stapokibart 600-mg loading dose, then 300 mg every 2 weeks up to 52 weeks. Efficacy outcomes included the proportions of patients with ≥ 50%/75%/90% improvements from baseline of parent trials in the Eczema Area and Severity Index, Investigator's Global Assessment, and weekly average of the daily Peak Pruritus Numerical Rating Scale. RESULTS: In total, 127 patients were enrolled, and 110 (86.6%) completed the study. At week 52, the Eczema Area and Severity Index-50/75/90 response rates were 96.3%, 87.9%, and 71.0%, respectively. An Investigator's Global Assessment 0/1 with a ≥ 2-point reduction was achieved in 39.3% of patients at week 16, increasing to 58.9% at week 52. The proportions of patients with ≥ 3-point and ≥ 4-point reductions in the weekly average of daily Peak Pruritus Numerical Rating Scale scores were 80.2% and 62.2%, respectively, at week 52. Improvement in patients' quality of life was sustained over a 52-week treatment period. Treatment-emergent adverse events occurred in 88.2% of patients, with an exposure-adjusted event rate of 299.2 events/100 patient-years. Coronavirus disease 2019, upper respiratory tract infection, and conjunctivitis were the most common treatment-emergent adverse events. CONCLUSIONS: Long-term treatment with stapokibart for 52 weeks showed high efficacy and good safety profiles, supporting its use as a continuous long-term treatment option for atopic dermatitis. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT04893707 (15 May, 2021).

Two {CuI[P4Mo6]2}-Based Coordination Polymers Incorporating In Situ Converted Tetrapyridyl Ligands for Trace Analysis of Nitrofuran Antibiotics.

Nitrofurans are important synthetic broad-spectrum antibacterial drugs with the basic structure of 5-nitrofuran. Due to their toxicity, it is essential to develop a sensitive sensor with strong anti-interference capabilities for their detection. In this work, two {P4Mo6O31}12--based compounds, [H4(HPTTP)]2{CuI[Mo12O24(OH)6(PO4)3(HPO4)(H2PO4)4]}·xH2O (x = 13 for (1), 7 for (2); HPTTP = 4,4',4″,4‴-(1H-pyrrole-2,3,4,5-tetrayl)tetrapyridine), exhibiting similar coordination but distinct stacking modes. Both compounds were synthesized and used for the electrochemical detection of nitrofuran antibiotics. The tetrapyridine-based ligand was generated in situ during assembly, and its potential mechanism was discussed. Composite electrode materials, formed by mixing graphite powder with compounds 1-2 and physically grinding them, proved to be highly effective in the electrochemical trace detection of furazolidone (FZD) and furaltadone hydrochloride (FTD·HCl) under optimal conditions. Besides, the possible electrochemical detection mechanisms of two nitro-antibiotics were studied.

Synaptotagmins family affect glucose transport in retinal pigment epithelial cells through their ubiquitination-mediated degradation and glucose transporter-1 regulation.

BACKGROUND: Synaptotagmins (SYTs) are a family of 17 membrane transporters that function as calcium ion sensors during the release of Ca2+-dependent neurotransmitters and hormones. However, few studies have reported whether members of the SYT family play a role in glucose uptake in diabetic retinopathy (DR) through Ca2+/glucose transporter-1 (GLUT1) and the possible regulatory mechanism of SYTs. AIM: To elucidate the role of the SYT family in the regulation of glucose transport in retinal pigment epithelial cells and explore its potential as a therapeutic target for the clinical management of DR. METHODS: DR was induced by streptozotocin in C57BL/6J mice and by high glucose medium in human retinal pigment epithelial cells (ARPE-19). Bioinformatics analysis, reverse transcriptase-polymerase chain reaction, Western blot, flow cytometry, ELISA, HE staining, and TUNEL staining were used for analysis. RESULTS: Six differentially expressed proteins (SYT2, SYT3, SYT4, SYT7, SYT11, and SYT13) were found between the DR and control groups, and SYT4 was highly expressed. Hyperglycemia induces SYT4 overexpression, manipulates Ca2+ influx to induce GLUT1 fusion with the plasma membrane, promotes abnormal expression of the glucose transporter GLUT1 and excessive glucose uptake, induces ARPE-19 cell apoptosis, and promotes DR progression. Parkin deficiency inhibits the proteasomal degradation of SYT4 in DR, resulting in SYT4 accumulation and enhanced GLUT1 fusion with the plasma membrane, and these effects were blocked by oe-Parkin treatment. Moreover, dysregulation of the myelin transcription factor 1 (Myt1)-induced transcription of SYT4 in DR further activated the SYT4-mediated stimulus-secretion coupling process, and this process was inhibited in the oe-MYT1-treated group. CONCLUSION: Our study reveals the key role of SYT4 in regulating glucose transport in retinal pigment epithelial cells during the pathogenesis of DR and the underlying mechanism and suggests potential therapeutic targets for clinical DR.

Predicting who has delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage using machine learning approach: a multicenter, retrospective cohort study.

BACKGROUND: Early prediction of delayed cerebral ischemia (DCI) is critical to improving the prognosis of aneurysmal subarachnoid hemorrhage (aSAH). Machine learning (ML) algorithms can learn from intricate information unbiasedly and facilitate the early identification of clinical outcomes. This study aimed to construct and compare the ability of different ML models to predict DCI after aSAH. Then, we identified and analyzed the essential risk of DCI occurrence by preoperative clinical scores and postoperative laboratory test results. METHODS: This was a multicenter, retrospective cohort study. A total of 1039 post-operation patients with aSAH were finally included from three hospitals in China. The training group contained 919 patients, and the test group comprised 120 patients. We used five popular machine-learning algorithms to construct the models. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, precision, and f1 score were used to evaluate and compare the five models. Finally, we performed a Shapley Additive exPlanations analysis for the model with the best performance and significance analysis for each feature. RESULTS: A total of 239 patients with aSAH (23.003%) developed DCI after the operation. Our results showed that in the test cohort, Random Forest (RF) had an AUC of 0.79, which was better than other models. The five most important features for predicting DCI in the RF model were the admitted modified Rankin Scale, D-Dimer, intracranial parenchymal hematoma, neutrophil/lymphocyte ratio, and Fisher score. Interestingly, clamping or embolization for the aneurysm treatment was the fourth button-down risk factor in the ML model. CONCLUSIONS: In this multicenter study, we compared five ML methods, among which RF performed the best in DCI prediction. In addition, the essential risks were identified to help clinicians monitor the patients at high risk for DCI more precisely and facilitate timely intervention.

Predicting major adverse cardiovascular events within 3 years by optimization of radiomics model derived from pericoronary adipose tissue on coronary computed tomography angiography: a case-control study.

BACKGROUND: Coronary inflammation induces changes in pericoronary adipose tissue (PCAT) can be detected by coronary computed tomography angiography (CCTA). Our aim was to investigate whether different PCAT radiomics model based on CCTA could improve the prediction of major adverse cardiovascular events (MACE) within 3 years. METHODS: This retrospective study included 141 consecutive patients with MACE and matched to patients with non-MACE (n = 141). Patients were randomly assigned into training and test datasets at a ratio of 8:2. After the robust radiomics features were selected by using the Spearman correlation analysis and the least absolute shrinkage and selection operator, radiomics models were built based on different machine learning algorithms. The clinical model was then calculated according to independent clinical risk factors. Finally, an overall model was established using the radiomics features and the clinical factors. Performance of the models was evaluated for discrimination degree, calibration degree, and clinical usefulness. RESULTS: The diagnostic performance of the PCAT model was superior to that of the RCA-model, LAD-model, and LCX-model alone, with AUCs of 0.723, 0.675, 0.664, and 0.623, respectively. The overall model showed superior diagnostic performance than that of the PCAT-model and Cli-model, with AUCs of 0.797, 0.723, and 0.706, respectively. Calibration curve showed good fitness of the overall model, and decision curve analyze demonstrated that the model provides greater clinical benefit. CONCLUSION: The CCTA-based PCAT radiomics features of three major coronary arteries have the potential to be used as a predictor for MACE. The overall model incorporating the radiomics features and clinical factors offered significantly higher discrimination ability for MACE than using radiomics or clinical factors alone.

Toward the Internet of Medical Things: Architecture, trends and challenges.

In recent years, the growing pervasiveness of wearable technology has created new opportunities for medical and emergency rescue operations to protect users' health and safety, such as cost-effective medical solutions, more convenient healthcare and quick hospital treatments, which make it easier for the Internet of Medical Things (IoMT) to evolve. The study first presents an overview of the IoMT before introducing the IoMT architecture. Later, it portrays an overview of the core technologies of the IoMT, including cloud computing, big data and artificial intelligence, and it elucidates their utilization within the healthcare system. Further, several emerging challenges, such as cost-effectiveness, security, privacy, accuracy and power consumption, are discussed, and potential solutions for these challenges are also suggested.

Coronary slow flow research: a bibliometric analysis.

BACKGROUND: Studies on coronary slow flow are receiving increasing attention, but objective evaluations are still lacking. The purpose of this study was to visualize the current status and research hotspots of coronary slow flow through bibliometric analysis. METHODS: All relevant publications on coronary slow flow from 2003 to 2022 were extracted from the Web of Science Core Collection database and analyzed by VOSviewer and CiteSpace visualization software. Year of publication, journal, country/region, institution, and first author of each paper, as well as research hotspots were identified. RESULTS: A total of 913 publications were retrieved. The journal with the most publications was Coronary Artery Disease. The country/region with the most publications was Turkey, followed by China and the United States. The institution with the largest publication volume was Turkey Specialized Higher Education Research Hospital. The author with the largest publication volume was Chun-Yan Ma from China. Keyword analysis indicated that "treatment and prognosis", "pathogenesis and risk factors" and "diagnosis" were the clustering centers of coronary slow flow, and the research hotspots gradually changed with time, from pathogenesis to treatment and prognosis. CONCLUSION: Future research will focus on the search for effective and non-invasive detection indicators and treatments of coronary slow flow. Collaboration needs to be enhanced between different institutions or countries/regions, which would improve clinical outcomes for patients with coronary slow flow.

Relationship between quantitative epicardial adipose tissue based on coronary computed tomography angiography and coronary slow flow.

BACKGROUND: The purpose of this study was to explore the relationship between quantitative epicardial adipose tissue (EAT) based on coronary computed tomography angiography (CCTA) and coronary slow flow (CSF). METHODS: A total of 85 patients with < 40% coronary stenosis on diagnostic coronary angiography were included in this retrospective study between January 2020 and December 2021. A semi-automatic method was developed for EAT quantification on CCTA images. According to the thrombolysis in myocardial infarction flow grade, the patients were divided into CSF group (n = 39) and normal coronary flow group (n = 46). Multivariate logistic regression was used to explore the relationship between EAT and CSF. Receiver operating characteristic (ROC) curve was plotted to evaluate the diagnostic value of EAT in CSF. RESULTS: EAT volume in the CSF group was significantly higher than that of the normal coronary flow group (128.83± 21.59 mL vs. 101.87± 18.56 mL, P < 0.001). There was no significant difference in epicardial fat attenuation index between the two groups (P > 0.05). Multivariate logistic regression analysis showed that EAT volume was independently related to CSF [odds ratio (OR) = 4.82, 95% confidence interval (CI): 3.06-7.27, P < 0.001]. The area under ROC curve for EAT volume in identifying CSF was 0.86 (95% CI: 0.77-0.95). The optimal cutoff value of 118.46 mL yielded a sensitivity of 0.80 and a specificity of 0.94. CONCLUSIONS: Increased EAT volume based on CCTA is strongly associated with CSF. This preliminary finding paves the way for future and larger studies aimed to definitively recognize the diagnostic value of EAT in CSF.

A Robust Deep Learning Framework Based on Spectrograms for Heart Sound Classification.

Heart sound analysis plays an important role in early detecting heart disease. However, manual detection requires doctors with extensive clinical experience, which increases uncertainty for the task, especially in medically underdeveloped areas. This paper proposes a robust neural network structure with an improved attention module for automatic classification of heart sound wave. In the preprocessing stage, noise removal with Butterworth bandpass filter is first adopted, and then heart sound recordings are converted into time-frequency spectrum by short-time Fourier transform (STFT). The model is driven by STFT spectrum. It automatically extracts features through four down sample blocks with different filters. Subsequently, an improved attention module based on Squeeze-and-Excitation module and coordinate attention module is developed for feature fusion. Finally, the neural network will give a category for heart sound waves based on the learned features. The global average pooling layer is adopted for reducing the model's weight and avoiding overfitting, while focal loss is further introduced as the loss function to minimize the data imbalance problem. Validation experiments have been conducted on two publicly available datasets, and the results well demonstrate the effectiveness and advantages of our method.

Cardiac LGE MRI Segmentation With Cross-Modality Image Augmentation and Improved U-Net.

Image segmentation is a challenging problem in imaging informatics, which stems from the intersection of imaging techniques, computer science and biomedicine. In particular, accurate segmentation of cardiac structures in late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) is of great clinical importance for cardiac function assessment and myocardial disease diagnosis. However, it is a well-known challenge due to its special imaging modality and the lack of labeled LGE samples. In this paper, we propose an unsupervised ventricular segmentation algorithm that can perform biventricular segmentation of LGE images in the absence of labeled LGE data. There are two primary modules, the data augmentation procedure and the segmentation network. The easily available annotated balanced-Steady State Free Precession (bSSFP) images are employed for cross-modal data augmentation by image translation, where a single bSSFP image is converted into multiple synthetic LGE images while preserving the original morphological structure. Then, the proposed segmentation network is trained with the synthetic LGE images and used for segmenting real LGE images. Validation experiments demonstrated the effectiveness and advantages of the proposed algorithm.

Selective and replicable neuroimaging-based indicators of pain discriminability.

Neural indicators of pain discriminability have far-reaching theoretical and clinical implications but have been largely overlooked previously. Here, to directly identify the neural basis of pain discriminability, we apply signal detection theory to three EEG (Datasets 1-3, total N = 366) and two fMRI (Datasets 4-5, total N = 399) datasets where participants receive transient stimuli of four sensory modalities (pain, touch, audition, and vision) and two intensities (high and low) and report perceptual ratings. Datasets 1 and 4 are used for exploration and others for validation. We find that most pain-evoked EEG and fMRI brain responses robustly encode pain discriminability, which is well replicated in validation datasets. The neural indicators are also pain selective since they cannot track tactile, auditory, or visual discriminability, even though perceptual ratings and sensory discriminability are well matched between modalities. Overall, we provide compelling evidence that pain-evoked brain responses can serve as replicable and selective neural indicators of pain discriminability.

Evaluation of left atrial reservoir function and sphericity index in patients with mitral valve disease: a cardiac magnetic resonance imaging study.

To assess the performance of biplane area-length method in measuring left atrial (LA) volume and sphericity index and to investigate the correlation of LA reservoir function and sphericity index with atrial fibrosis on cardiac magnetic resonance imaging (MRI) in patients with mitral valve disease (MVD). Forty-eight patients with MVD undergoing cardiac MRI scan were enrolled in this retrospective study. LA reservoir function, measured as maximum volume (LAVmax), minimum volume (LAVmin) and ejection fraction (LAEF), and sphericity index were quantified using the biplane area-length method and standard short-axis approach, respectively. Comparisons of LA reservoir function and sphericity index between the two different methods were performed, as well as between positive (n = 17, 35%) and negative atrial wall fibrosis groups (n = 31, 65%). There was no difference in the values of LAVmax index and sphericity index between the two different methods. The biplane area-length method had poor performance in assessing LAVmin index and LAEF compared to standard short-axis approach, with an underestimation of 13.5% for LAVmin index and an overestimation of 27% for LAEF. Patients with positive atrial fibrosis had larger LAVmax index, LAVmin index and sphericity index, and lower LAEF levels in comparison to the negative atrial fibrosis group. The biplane area-length method has good performance in assessing LA sphericity index for patients with MVD, not in LA reservoir function. Patients with positive atrial fibrosis tend to suffer from more adverse LA remodelling.

Reward learning in the development and maintenance of chronic back pain.

Treating and preventing chronic pain require our understanding of how it develops and maintains. Löffler et al. (2022)1 demonstrate that distinct operant learning signals in the vmPFC-NAc pathway predict the development and maintenance of chronic back pain.

Relationship between epicardial fat volume on cardiac CT and atherosclerosis severity in three-vessel coronary artery disease: a single-center cross-sectional study.

BACKGROUND: The ideal treatment strategy for stable three-vessel coronary artery disease (CAD) patients are difficult to determine and for patients undergoing conservative treatment, imaging evidence of coronary atherosclerotic severity progression remains limited. Epicardial fat volume (EFV) on coronary CT angiography (CCTA) has been considered to be associated with coronary atherosclerosis. Therefore, this study aims to evaluate the relationship between EFV level and coronary atherosclerosis severity in three-vessel CAD. METHODS: This retrospective study enrolled 252 consecutive patients with three-vessel CAD and 252 normal control group participants who underwent CCTA between January 2018 and December 2019. A semi-automatic method was developed for EFV quantification on CCTA images, standardized by body surface area. Coronary atherosclerosis severity was evaluated and scored by the number of coronary arteries with ≥ 50% stenosis on coronary angiography. Patients were subdivided into groups on the basis of lesion severity: mild (score = 3 vessels, n = 85), moderate (3.5 vessels ≤ score < 4 vessels, n = 82), and severe (4 vessels ≤ score ≤ 7 vessels, n = 85). The independent sample t-test, analysis of variance, and logistic regression analysis were used to evaluate the associations between EFV level and severity of coronary atherosclerosis. RESULTS: Compared with normal controls, three-vessel CAD patients had significantly higher EFV level (65 ± 22 mL/m2 vs. 48 ± 19 mL/m2; P < 0.001). In patients with three-vessel CAD, there was a progressive decline in EFV level as the score of coronary atherosclerosis severity increased, especially in those patients with a body mass index (BMI) ≥ 25 kg/m2 (75 ± 21 mL/m2 vs. 72 ± 22 mL/m2 vs. 62 ± 17 mL/m2; P < 0.05). Multivariable regression analysis showed that both BMI (OR 3.40, 95% CI 2.00-5.78, P < 0.001) and the score of coronary atherosclerosis severity (OR 0.49, 95% CI 0.26-0.93, P < 0.05) were independently related to the change of EFV level. CONCLUSION: Three-vessel CAD patients do have higher EFV level than the normal controls. While, there may be an inverse relationship between EFV level and the severity of coronary atherosclerosis in patients with three-vessel CAD.

Improving protected area effectiveness through consideration of different human-pressure baselines.

Previous assessments of the effectiveness of protected areas (PAs) focused primarily on changes in human pressure over time and did not consider the different human-pressure baselines of PAs, thereby potentially over- or underestimating PA effectiveness. We developed a framework that considers both human-pressure baseline and change in human pressure over time and assessed the effectiveness of 338 PAs in China from 2010 to 2020. The initial state of human pressure on PAs was taken as the baseline, and changes in human pressure index (HPI) were further analyzed under different baselines. We used the random forest models to identify the management measures that most improved effectiveness in resisting human pressure for the PAs with different baselines. Finally, the relationships between the changes in the HPI and the changes in natural ecosystems in PAs were analyzed with different baselines. Of PAs with low HPI baselines, medium HPI baselines, and high HPI baselines, 76.92% (n=150), 11.11% (n=12), and 22.86% (n=8) , respectively, showed positive effects in resisting human pressure. Overall, ignoring human-pressure baselines somewhat underestimated the positive effects of PAs, especially for those with low initial human pressure. For PAs with different initial human pressures, different management measures should be taken to improve effectiveness and reduce threats to natural ecosystems. We believe our framework is useful for assessing the effectiveness of PAs globally, and we recommend it be included in the Convention on Biological Diversity Post-2020 Strategy.

Las evaluaciones previas de la efectividad de las áreas protegidas (AP) se han enfocado principalmente en los cambios de las presiones humanas con el tiempo y no han considerado las diferentes líneas base de las presiones humanas en las AP, por lo que potencialmente han sobrestimado o subestimado su efectividad. Desarrollamos un marco de trabajo que considera las líneas base de presión humana y los cambios de las presiones humanas con el tiempo y evaluamos a la efectividad de 338 AP en China entre 2010 y 2020. Consideramos el estado inicial de la presión humana en las AP como la línea base y analizamos los cambios en el índice de presión humana (IPH) bajo diferentes líneas base. Utilizamos modelos de bosque aleatorio para identificar las medidas de gestión que más aumentaron la efectividad de la resistencia a las presiones humanas en las AP con líneas base diferentes. Finalmente, analizamos con diferentes líneas base las relaciones entre los cambios en el IPH y los cambios en los ecosistemas naturales de las AP. De las AP con líneas base de IPH bajas, medianas y altas, 76.92% (n=150), 11.11% (n=12) y 22.86% (n=8), respectivamente, mostraron efectos positivos de resistencia a las presiones humanas. En general, si ignoramos las líneas base de las presiones humanas, se subestiman los efectos positivos de las AP de una u otra manera, especialmente aquellas con poca presión humana al inicio. En el caso de las AP que al inicio tienen diferentes presiones humanas, se deben tomar diferentes medidas de gestión para mejorar la efectividad y reducir las amenazas a los ecosistemas naturales. Creemos que nuestro marco de trabajo sirve para evaluar la efectividad mundial de las AP y recomendamos que se incluya en la Estrategia Post-2020 de la Convención sobre la Diversidad Biológica. Mejoría de la Efectividad de un Área Protegida al Considerar Diferentes Líneas Base de Presión Humana.

[Non-invasive electrical neuromodulation techniques: analgesic effects and neural mechanisms].

As non-pharmaceutical interventions, non-invasive electrical neuromodulation techniques are promising in pain management. With many advantages, such as low costs, high usability, and non-invasiveness, they have been exploited to treat multiple types of clinical pain. Proper use of these techniques requires a comprehensive understanding of how they work. In this article, we reviewed recent studies concerning non-invasive electrical peripheral nerve stimulation (transcutaneous electrical nerve stimulation and transcutaneous vagus/vagal nerve stimulation) as well as electrical central nerve stimulation (transcranial direct current stimulation and transcranial alternating current stimulation). Specifically, we discussed their analgesic effects on acute and chronic pain, and the neural mechanisms thereof. We then contrasted the four kinds of nerve stimulation techniques, pointing out limitations of existing studies and proposing directions for future research. With more extensive and in-depth research to overcome these limitations, we shall witness more clinical applications of non-invasive electrical nerve stimulations to alleviate patients' pain and ease the crippling medical and economic burden imposed on patients, their families, and the entire society.

Successful recovery of COVID-19-associated recurrent diarrhea and gastrointestinal hemorrhage using convalescent plasma.

BACKGROUND: Gastrointestinal symptoms are not rare among coronavirus disease 2019 (COVID-19) patients, but there have been no reports regarding convalescent plasma therapy for the recovery of gastrointestinal problems in COVID-19 patients. CASE PRESENTATION: We present two cases of patients with COVID-19-associated recurrent diarrhea and positive fecal occult blood who successfully recovered after a one-time convalescent plasma administration. CONCLUSION: When COVID-19 patients develop recurrent or refractory gastrointestinal symptoms and fail to respond to the available treatment, alternative therapy with convalescent plasma administration may be considered.

[Establishment of new evaluation standards for systemic anaphylactoid reactions using mouse model].

The detection of drug-induced anaphylactoid reactions remains a global challenge,still lacking mature and reliable animal models or test methods. Therefore,the purpose of this paper is to explore and establish the test methods and evaluation standards for anaphylactoid reactions that apply to injection drugs. Based on the anaphylactoid reaction symptoms of mice induced by intravenous injection drugs C48/40 and Tween 80,a list of systemic anaphylactoid reaction symptoms in mice was sorted out and an evaluation standard of anaphylactoid reactions symptoms was established by applying symptom intensity coefficient K( that can represent these verity of anaphylactoid reaction symptoms) and its calculation formula Accordingly,histamine,tryptase,and Ig E were selected as blood indicators of anaphylactoid reactions,so that a test method combining symptoms evaluation and blood makers detection was established.This test method could be used to evaluate the characteristics of anaphylactoid reactions: coefficient K,blood histamine levels were highly and positively correlated with C48/80 and Tween 80 dose; The log value of histamine was highly and positively correlated with K; tryptase level may rise,or remain steady,or drop,possibly associated with the characteristics of the tested object and time for blood taking; and Ig E level would drop or remain steady,but it would not rise,which can be clearly distinguished from type I allergic reactions. On this basis,tiohexol,iopromide,paclitaxel,Xuesaitong Injection,Shuanghuanglian Injection and Shengmai Injection were used to investigate the applicability. The testing results showed a high degree of consistency with the actual clinical situation. The results suggest that the method of systemic anaphylaxis test in mice has high sensitivity,specificity and good consistency with clinical practice.It is suggested to be further validated and popularized.