AT(N) biomarker profiles and Alzheimer's disease symptomology in Down syndrome.

INTRODUCTION: Down syndrome (DS) is a genetic cause of early-onset Alzheimer's disease (AD). The National Institute on Aging-Alzheimer's Association AT(N) Research Framework is a staging model for AD biomarkers but has not been assessed in DS. METHOD: Data are from the Alzheimer's Biomarker Consortium-Down Syndrome. Positron emission tomography (PET) amyloid beta (Aß; 15 mCi of [11 C]Pittsburgh compound B) and tau (10 mCi of [18 F]AV-1451) were used to classify amyloid (A) -/+ and tau (T) +/-. Hippocampal volume classified neurodegeneration (N) -/+. The modified Cued Recall Test assessed episodic memory. RESULTS: Analyses included 162 adults with DS (aged M = 38.84 years, standard deviation = 8.41). Overall, 69.8% of participants were classified as A-/T-/(N)-, 11.1% were A+/T-/(N)-, 5.6% were A+/T+/(N)-, and 9.3% were A+/T+/(N)+. Participants deemed cognitively stable were most likely to be A-T-(N)- and A+T-(N)-. Tau PET (T+) most closely aligning with memory impairment and AD clinical status. DISCUSSION: Findings add to understanding of AT(N) biomarker profiles in DS. HIGHLIGHTS: Overall, 69.8% of adults with Down syndrome (DS) aged 25 to 61 years were classified as amyloid (A)-/tau (T)-/neurodegeneration (N)-, 11.1% were A+/T-/(N)-, 5.6% were A+/T+/(N)-, and 9.3% were A+/T+/(N)+. The AT(N) profiles were associated with clinical Alzheimer's disease (AD) status and with memory performance, with the presence of T+ aligned with AD clinical symptomology. Findings inform models for predicting the transition to the prodromal stage of AD in DS.

Timing of Alzheimer's Disease by Intellectual Disability Level in Down Syndrome.

BACKGROUND: Trisomy 21 causes Down syndrome (DS) and is a recognized cause of early-onset Alzheimer's disease (AD). OBJECTIVE: The current study sought to determine if premorbid intellectual disability level (ID) was associated with variability in age-trajectories of AD biomarkers and cognitive impairments. General linear mixed models compared the age-trajectory of the AD biomarkers PET Aß and tau and cognitive decline across premorbid ID levels (mild, moderate, and severe/profound), in models controlling trisomy type, APOE status, biological sex, and site. METHODS: Analyses involved adults with DS from the Alzheimer's Biomarkers Consortium-Down Syndrome. Participants completed measures of memory, mental status, and visuospatial ability. Premorbid ID level was based on IQ or mental age scores prior to dementia concerns. PET was acquired using [11C] PiB for Aß, and [18F] AV-1451 for tau. RESULTS: Cognitive data was available for 361 participants with a mean age of 45.22 (SD = 9.92) and PET biomarker data was available for 154 participants. There was not a significant effect of premorbid ID level by age on cognitive outcomes. There was not a significant effect of premorbid ID by age on PET Aß or on tau PET. There was not a significant difference in age at time of study visit of those with mild cognitive impairment-DS or dementia by premorbid ID level. CONCLUSION: Findings provide robust evidence of a similar time course in AD trajectory across premorbid ID levels, laying the groundwork for the inclusion of individuals with DS with a variety of IQ levels in clinical AD trials.

The predictive value of deep learning-based cardiac ultrasound flow imaging for hypertrophic cardiomyopathy complicating arrhythmias.

OBJECTIVE: To investigate the predictive value of deep learning-based cardiac ultrasound flow imaging for hypertrophic cardiomyopathy (HCM) complicated by arrhythmias. METHODS: The clinical data of 158 patients with hypertrophic cardiomyopathy were retrospectively collected from July 2019 to December 2021, and additionally divided into training group 106 cases, validation group 26 cases and test group 26 cases according to the ratio of 4:1:1, and divided into concurrent and non-concurrent groups according to whether they were complicated by arrhythmia or not, respectively. General data of patients (age, gender, BMI, systolic blood pressure, diastolic blood pressure, HR) were collected, a deep learning model for cardiac ultrasound flow imaging was established, and image data, LVEF, LAVI, E/e', vortex area change rate, circulation intensity change rate, mean blood flow velocity, and mean EL value were extracted. RESULTS: The differences in general data (age, gender, BMI, systolic blood pressure, diastolic blood pressure, HR) between the three groups were not statistically significant, P > 0.05. The differences in age, gender, BMI, systolic blood pressure, diastolic blood pressure, HR between the patients in the concurrent and non-concurrent groups in the training group were not statistically significant, P > 0.05. CONCLUSIONS: Deep learning-based cardiac ultrasound flow imaging can identify cardiac ultrasound images more accurately and has a high predictive value for arrhythmias complicating hypertrophic cardiomyopathy, and vortex area change rate, circulation intensity change rate, mean flow velocity, mean EL, LAVI, and E/e' are all risk factors for arrhythmias complicating hypertrophic cardiomyopathy.

Label-Free Differentiation of Cancer and Non-Cancer Cells Based on Machine-Learning-Algorithm-Assisted Fast Raman Imaging.

This paper proposes a rapid, label-free, and non-invasive approach for identifying murine cancer cells (B16F10 melanoma cancer cells) from non-cancer cells (C2C12 muscle cells) using machine-learning-assisted Raman spectroscopic imaging. Through quick Raman spectroscopic imaging, a hyperspectral data processing approach based on machine learning methods proved capable of presenting the cell structure and distinguishing cancer cells from non-cancer muscle cells without compromising full-spectrum information. This study discovered that biomolecular information-nucleic acids, proteins, and lipids-from cells could be retrieved efficiently from low-quality hyperspectral Raman datasets and then employed for cell line differentiation.

Clinical Study on Long-Term Sinus Reversion Rate and Left Atrial Function Recovery of Mitral Valve Disease with Atrial Fibrillation under Modified Surgical Radiofrequency Ablation.

We aimed to study the long-term sinus reversion rate and recovery of left atrial function after modified surgical radiofrequency ablation for permanent atrial fibrillation caused by mitral valve disease. From March 2014 to May 2020, 35 patients who underwent modified surgical radiofrequency ablation during cardiac valve surgery in our hospital were selected as the study group, and 25 normal individuals without cardiac structural changes were selected as the control group. The time of modified surgical radiofrequency ablation and long-term sinus reversion rate were measured, and left atrial anteroposterior, superoinferior, left and right diameters, left atrial ejection fraction, left atrial filling index, and left atrial ejection force were measured before and 6 months after surgery. The mean ablation time was 23.2 min, and the long-term sinus reversion rate was 80.0%. The left atrium diameter decreased and the left atrium ejection fraction increased after the operation (P < 0.05). The left atrium filling index and ejection force were significantly increased in 28 patients with sinus reversion (P < 0.05). The decrease in left atrial diameter and the increase in left atrial ejection fraction were correlated with sinus conversion after surgery (P < 0.05). The modified operation is simple, the curative effect is definite, and the sinus reversion rate is high, which is beneficial to the restoration of left atrial structure, ejection function, and hemodynamic function.

Predicting Breast Cancer in Breast Imaging Reporting and Data System (BI-RADS) Ultrasound Category 4 or 5 Lesions: A Nomogram Combining Radiomics and BI-RADS.

Radiomics reflects the texture and morphological features of tumours by quantitatively analysing the grey values of medical images. We aim to develop a nomogram incorporating radiomics and the Breast Imaging Reporting and Data System (BI-RADS) for predicting breast cancer in BI-RADS ultrasound (US) category 4 or 5 lesions. From January 2017 to August 2018, a total of 315 pathologically proven breast lesions were included. Patients from the study population were divided into a training group (n = 211) and a validation group (n = 104) according to a cut-off date of March 1st, 2018. Each lesion was assigned a category (4A, 4B, 4C or 5) according to the second edition of the American College of Radiology (ACR) BI-RADS US. A radiomics score was generated from the US image. A nomogram was developed based on the results of multivariate regression analysis from the training group. Discrimination, calibration and clinical usefulness of the nomogram for predicting breast cancer were assessed in the validation group. The radiomics score included 9 selected radiomics features. The radiomics score and BI-RADS category were independently associated with breast malignancy. The nomogram incorporating the radiomics score and BI-RADS category showed better discrimination (area under the receiver operating characteristic curve [AUC]: 0.928; 95% confidence interval [CI]: 0.876, 0.980) between malignant and benign lesions than either the radiomics score (P = 0.029) or BI-RADS category (P = 0.011). The nomogram demonstrated good calibration and clinical usefulness. In conclusion, the nomogram combining the radiomics score and BI-RADS category is potentially useful for predicting breast malignancy in BI-RADS US category 4 or 5 lesions.

[Explore new clinical application of Huanglian and corresponding compound prescriptions from their traditional use].

Huanglian is a commonly used Chinese medicinal herb in the ancient and the present. It has a history of 2000 years in clinical application, having the efficacy of "Clear away heat and remove dampness, purge the sthenic fire and eliminate toxic materials", therefore can be used for various diseases or syndromes in types of dampness-heat and fire-toxin by internal or external use. Compound prescriptions mainly based on Huanglian or prescribed by Huanglian, such as Puji Xiaodu Yin, Huanglian Jiedu Tang, Zhusha Anshen Wan, Qingying Tang, Angong Niuhuang Wan, Niuhuang Qingxin Wan, Jiaotai Wan, Huanglian Ejiao Tang, Zuojin Wan, Danggui Longhui Wan, Huanglian Yanggan Wan, Wu Xiexin Tang, Lianpu Yin, Gegen Huangqin Huanglian Tang, Baitouweng Tang, Xianglian Wan etc. All of these are well-known formulas for clearing away toxin of heat-fire of heart and liver, as well as dampness-heat of stomach and intestines. Nowadays, Huanglian is generally considered as a kind of antibiotic and antivirus herb and is widely used for many infective diseases. In fact, it is also used to cure cardiovascular and cerebrovascular diseases, diabetes and cancer based on pharmacological studies. Having been using Huanglian in treating the above diseases and having conducted clinical and experimental research on cancer and liver diseases, the author observed that Huanglian and its compound prescriptions have obvious effects on liver diseases such as acute or chronic hepatitis, liver fibrosis, liver cirrhosis and liver cancer due to types of dampness-heat and fire-toxin. Part of the effects has been proved by experimental research and it is worth carrying out more research in this area for development and clinical application.