A real-world observation of antipsychotic effects on brain volumes and intrinsic brain activity in schizophrenia

Background: The confounding effects of antipsychotics that led to the inconsistencies of neuroimaging findings have long been the barriers to understanding the pathophysiology of schizophrenia (SZ). Although it is widely accepted that antipsychotics can alleviate psychotic symptoms during the early most acute phase, the longer-term effects of antipsychotics on the brain have been unclear. This study aims to look at the susceptibility of different imaging measures to longer-term medicated status through real-world observation. Methods: We compared gray matter volume (GMV) with amplitude of low-frequency fluctuations (ALFFs) in 89 medicated-schizophrenia (med-SZ), 81 unmedicated-schizophrenia (unmed-SZ), and 235 healthy controls (HC), and the differences were explored for relationships between imaging modalities and clinical variables. We also analyzed age-related effects on GMV and ALFF values in the two patient groups (med-SZ and unmed-SZ). Results: Med-SZ demonstrated less GMV in the prefrontal cortex, temporal lobe, cingulate gyri, and left insula than unmed-SZ and HC ( Conclusion: GMV loss appeared to be pronounced to longer-term antipsychotics, whereby imbalanced alterations in regional low-frequency fluctuations persisted unaffected by antipsychotic treatment. Our findings may help to understand the disease course of SZ and potentially identify a reliable neuroimaging feature for diagnosis

Microembossing: A Convenient Process for Fabricating Microchannels on Nanocellulose Paper-Based Microfluidics.

Nanopaper, derived from nanofibrillated cellulose, has generated considerable interest as a promising material for microfluidic applications. Its appeal lies in a range of excellent qualities, including an exceptionally smooth surface, outstanding optical transparency, a uniform nanofiber matrix with nanoscale porosity, and customizable chemical properties. Despite the rapid growth of nanopaper-based microfluidics, the current techniques used to create microchannels on nanopaper, such as 3D printing, spray coating, or manual cutting and assembly, which are crucial for practical applications, still possess certain limitations, notably susceptibility to contamination. Furthermore, these methods are restricted to the production of millimeter-sized channels. This study introduces a straightforward process that utilizes convenient plastic micro-molds for simple microembossing operations to fabricate microchannels on nanopaper, achieving a minimum width of 200 µm. The developed microchannel outperforms existing approaches, achieving a fourfold improvement, and can be fabricated within 45 min. Furthermore, fabrication parameters have been optimized, and a convenient quick-reference table is provided for application developers. The proof-of-concept for a laminar mixer, droplet generator, and functional nanopaper-based analytical devices (NanoPADs) designed for Rhodamine B sensing using surface-enhanced Raman spectroscopy was demonstrated. Notably, the NanoPADs exhibited exceptional performance with improved limits of detection. These outstanding results can be attributed to the superior optical properties of nanopaper and the recently developed accurate microembossing method, enabling the integration and fine-tuning of the NanoPADs

Deep Learning for Microfluidic-Assisted <i>Caenorhabditis elegans</i> Multi-Parameter Identification Using YOLOv7

The Caenorhabditis elegans (C. elegans) is an ideal model organism for studying human diseases and genetics due to its transparency and suitability for optical imaging. However, manually sorting a large population of C. elegans for experiments is tedious and inefficient. The microfluidic-assisted C. elegans sorting chip is considered a promising platform to address this issue due to its automation and ease of operation. Nevertheless, automated C. elegans sorting with multiple parameters requires efficient identification technology due to the different research demands for worm phenotypes. To improve the efficiency and accuracy of multi-parameter sorting, we developed a deep learning model using You Only Look Once (YOLO)v7 to detect and recognize C. elegans automatically. We used a dataset of 3931 annotated worms in microfluidic chips from various studies. Our model showed higher precision in automated C. elegans identification than YOLOv5 and Faster R-CNN, achieving a mean average precision (mAP) at a 0.5 intersection over a union ([email protected]) threshold of 99.56%. Additionally, our model demonstrated good generalization ability, achieving an [email protected] of 94.21% on an external validation set. Our model can efficiently and accurately identify and calculate multiple phenotypes of worms, including size, movement speed, and fluorescence. The multi-parameter identification model can improve sorting efficiency and potentially promote the development of automated and integrated microfluidic platforms

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke — the second leading cause of death worldwide — were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries

A Novel Label-Free Biosensor for Detection of HE4 in Urine Based on Localized Surface Plasmon Resonance and Protein G Directional Fixed

A non-invasive and more sensitive method for detection of HE4 is very important for the early screening and detection of ovarian carcinoma. In this study, we improved our previous localized surface plasmon resonance (LSPR) biosensor for detection of HE4 in urine to overcome disadvantages of conventional methods. Protein G directional fixed method was firstly used for LSPR biosensor to improved sensitivity, and standard HE4 and clinical samples were detected separately using this new biosensor. Compared to our previous LSPR biosensor, this new sensor was more sensitive, with other advantages as before. Under optimum conditions, this new biosensor could display a detection limit of 1 pM and wide dynamic range of 1 pM to 10,000 pM. This new biosensor was effective for detection of HE4 in urine of early ovarian cancer patients, without label and purification. To the best of our knowledge, this is first work to investigate LSPR biosensor for detection of tumor marker in urine, with great advantages and clinical application potentials