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).

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.

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.

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).

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 Ca-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 Ca/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.

Two {Cu[PMo]}-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 {PMoO}-based compounds, [H(HPTTP)]{Cu[MoO(OH)(PO)(HPO)(HPO)]}·HO ( = 13 for (), 7 for (); HPTTP = 4,4',4″,4‴-(1-pyrrole-2,3,4,5-tetrayl)tetrapyridine), exhibiting similar coordination but distinct stacking modes.

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.

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.

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.

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.

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.

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 (LAV), minimum volume (LAV) and ejection fraction (LAEF), and sphericity index were quantified using the biplane area-length method and standard short-axis approach, respectively.

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) 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.

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.

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.

[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.

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.

Effect of mesenchymal stromal (stem) cell (MSC) transplantation in asthmatic animal models: A systematic review and meta-analysis.

Background: Over the years, mesenchymal stromal (stem) cells (MSCs) have been pre-clinically applied in the treatment of variety kinds of diseases including asthma and chronic lung diseases. Aim of the current study was to systematically review and to conduct meta-analysis on the published studies of MSC treatment in asthma animal models.

Methods: Publications on the MSC and asthma treatment was thoroughly searched in the electronic databases.

Calpain-GRIP Signaling in Nucleus Accumbens Core Mediates the Reconsolidation of Drug Reward Memory.

Exposure to drug-paired cues causes drug memories to be in a destabilized state and interfering with memory reconsolidation can inhibit relapse. Calpain, a calcium-dependent neutral cysteine protease, is involved in synaptic plasticity and the formation of long-term fear memory. However, the role of calpain in the reconsolidation of drug reward memory is still unknown.