Hypercomplex Graph Neural Network: Towards Deep Intersection of Multi-modal Brain Networks.

The multi-modal neuroimage study has provided insights into understanding the heteromodal relationships between brain network organization and behavioral phenotypes. Integrating data from various modalities facilitates the characterization of the interplay among anatomical, functional, and physiological brain alterations or developments. Graph Neural Networks (GNNs) have recently become popular in analyzing and fusing multi-modal, graph-structured brain networks.

Effectiveness of the frailty index in predicting recurrent pneumonia and death in long-term hospitalized patients with vascular cognitive impairment.

Objective: The aim was to predict the effectiveness of using frailty, defined by the frailty index (FI), for predicting recurrent pneumonia and death in patients 50 years and older with vascular cognitive impairment (VCI) during long-term hospitalization.

Measurements: This retrospective cohort study was conducted at a teaching hospital in western China and included VCI patients aged ≥50 years undergoing long-term hospitalization. The relevant data were collected from the electronic medical record system.

Accelerating denoising diffusion probabilistic model via truncated inverse processes for medical image segmentation.

Medical image segmentation demands precise accuracy and the capability to assess segmentation uncertainty for informed clinical decision-making. Denoising Diffusion Probability Models (DDPMs), with their advancements in image generation, can treat segmentation as a conditional generation task, providing accurate segmentation and uncertainty estimation. However, current DDPMs used in medical image segmentation suffer from low inference efficiency and prediction errors caused by excessive noise at the end of the forward process.

BrainMass: Advancing Brain Network Analysis for Diagnosis With Large-Scale Self-Supervised Learning.

Foundation models pretrained on large-scale datasets via self-supervised learning demonstrate exceptional versatility across various tasks. Due to the heterogeneity and hard-to-collect medical data, this approach is especially beneficial for medical image analysis and neuroscience research, as it streamlines broad downstream tasks without the need for numerous costly annotations. However, there has been limited investigation into brain network foundation models, limiting their adaptability and generalizability for broad neuroscience studies.

Ultra-stable insulin-glucagon fusion protein exploits an endogenous hepatic switch to mitigate hypoglycemic risk.

Unlabelled: The risk of hypoglycemia and its serious medical sequelae restrict insulin replacement therapy for diabetes mellitus. Such adverse clinical impact has motivated development of diverse glucose-responsive technologies, including algorithm-controlled insulin pumps linked to continuous glucose monitors ("closed-loop systems") and glucose-sensing ("smart") insulins. These technologies seek to optimize glycemic control while minimizing hypoglycemic risk.

A Chebyshev Confidence Guided Source-Free Domain Adaptation Framework for Medical Image Segmentation.

Source-free domain adaptation (SFDA) aims to adapt models trained on a labeled source domain to an unlabeled target domain without access to source data. In medical imaging scenarios, the practical significance of SFDA methods has been emphasized due to data heterogeneity and privacy concerns. Recent state-of-the-art SFDA methods primarily rely on self-training based on pseudo-labels (PLs).

CReg-KD: Model refinement via confidence regularized knowledge distillation for brain imaging.

One of the core challenges of deep learning in medical image analysis is data insufficiency, especially for 3D brain imaging, which may lead to model over-fitting and poor generalization. Regularization strategies such as knowledge distillation are powerful tools to mitigate the issue by penalizing predictive distributions and introducing additional knowledge to reinforce the training process. In this paper, we revisit knowledge distillation as a regularization paradigm by penalizing attentive output distributions and intermediate representations.

The effect of externally applied traditional Chinese medicine in diabetic foot: A systematic review and meta-analysis of 34 RCTs.

Aim: This study aimed to investigate the clinical efficacy of externally applied Traditional Chinese Medicine (TCM) on diabetic foot.

Methods: We searched the China Knowledge Network (CNKI), Wanfang Database, PubMed and Web of Science from inception to July 31, 2022, to find all randomized control trials (RCTs) related to externally applied TCMs in diabetic foot treatment. Information about the total effective rate, healing rate, and healing time were extracted.

Prognostic value of sarcopenia in older adults with transcatheter aortic valve implantation: A systematic review and meta-analysis.

Objective: We conducted this systematic review and meta-analysis to summarize the prevalence of sarcopenia and its impact on mortality in patients undergoing TAVI.

Method: Medline, EMBASE, and PubMed were searched from inception to October 14, 2022 to retrieve eligible studies that assessed sarcopenia in patients undergoing TAVI. Pooled sarcopenia prevalence was calculated with 95% confidence interval (CI), and heterogeneity was estimated using the I test.

Mapping Multi-Modal Brain Connectome for Brain Disorder Diagnosis via Cross-Modal Mutual Learning.

Recently, the study of multi-modal brain connectome has recorded a tremendous increase and facilitated the diagnosis of brain disorders. In this paradigm, functional and structural networks, e.g.

A deep connectome learning network using graph convolution for connectome-disease association study.

Multivariate analysis approaches provide insights into the identification of phenotype associations in brain connectome data. In recent years, deep learning methods including convolutional neural network (CNN) and graph neural network (GNN), have shifted the development of connectome-wide association studies (CWAS) and made breakthroughs for connectome representation learning by leveraging deep embedded features. However, most existing studies remain limited by potentially ignoring the exploration of region-specific features, which play a key role in distinguishing brain disorders with high intra-class variations, such as autism spectrum disorder (ASD), and attention deficit hyperactivity disorder (ADHD).

Tenuous transcriptional threshold of human sex determination. II. SRY exploits water-mediated clamp at the edge of ambiguity.

Y-encoded transcription factor SRY initiates male differentiation in therian mammals. This factor contains a high-mobility-group (HMG) box, which mediates sequence-specific DNA binding with sharp DNA bending. A companion article in this issue described sex-reversal mutations at box position 72 (residue 127 in human SRY), invariant as Tyr among mammalian orthologs.

Ensemble learning supervision augmentation for white matter hyperintensity segmentation.

Since the ambiguous boundary of the lesion and inter-observer variability, white matter hyperintensity segmentation annotations are inherently noisy and uncertain. On the other hand, the high capacity of deep neural networks (DNN) enables them to overfit labels with noise and uncertainty, which may lead to biased models with weak generalization ability. This challenge has been addressed by leveraging multiple annotations per image.

MRF-Net: A multi-branch residual fusion network for fast and accurate whole-brain MRI segmentation.

Whole-brain segmentation from T1-weighted magnetic resonance imaging (MRI) is an essential prerequisite for brain structural analysis, e.g., locating morphometric changes for brain aging analysis.

Brain age vector: A measure of brain aging with enhanced neurodegenerative disorder specificity.

Neuroimaging-driven brain age estimation has become popular in measuring brain aging and identifying neurodegenerations. However, the single estimated brain age (gap) compromises regional variations of brain aging, losing spatial specificity across diseases which is valuable for early screening. In this study, we combined brain age modeling with Shapley Additive Explanations to measure brain aging as a feature contribution vector underlying spatial pathological aging mechanism.

Peptide Model of the Mutant Proinsulin Syndrome. I. Design and Clinical Correlation.

The mutant proinsulin syndrome is a monogenic cause of diabetes mellitus due to toxic misfolding of insulin's biosynthetic precursor. Also designated (MIDY), this syndrome defines molecular determinants of foldability in the endoplasmic reticulum (ER) of β-cells. Here, we describe a peptide model of a key proinsulin folding intermediate and variants containing representative clinical mutations; the latter perturb invariant core sites in native proinsulin (Leu→Pro, Leu→Pro, and Phe→Ser).

Peptide Model of the Mutant Proinsulin Syndrome. II. Nascent Structure and Biological Implications.

Toxic misfolding of proinsulin variants in β-cells defines a monogenic diabetes syndrome, designated (MIDY). In our first study (previous article in this issue), we described a one-disulfide peptide model of a proinsulin folding intermediate and its use to study such variants. The mutations (Leu→Pro, Leu→Pro, and Phe→Ser) probe residues conserved among vertebrate insulins.

Insertion of a synthetic switch into insulin provides metabolite-dependent regulation of hormone-receptor activation.

Insulin-signaling requires conformational change: whereas the free hormone and its receptor each adopt autoinhibited conformations, their binding leads to structural reorganization. To test the functional coupling between insulin's "hinge opening" and receptor activation, we inserted an artificial ligand-dependent switch into the insulin molecule. Ligand-binding disrupts an internal tether designed to stabilize the hormone's native closed and inactive conformation, thereby enabling productive receptor engagement.

Theoretical prediction on photoelectric and supramolecular properties of benzoquinone-tetrathiafulvalene macrocyclic molecules.

Benzoquinone has the ability to serve as an electron acceptor, and tetrathiafulvalene has the ability to serve as an electron donor. Based on the facts above, this work creatively cycles the benzoquinone unit and the tetrathiafulvalene unit alternately into macrocyclic molecules, the cyclopolymers of benzoquinone-tetrafluorene (C[n]QTTF, n = 3~6). To explore their structure and properties, the M06-2X functional of density functional theory (DFT) with 6-311g(d) basis set was used to optimize the ground-state structures of C[n]QTTF.

Evolution of insulin at the edge of foldability and its medical implications.

Proteins have evolved to be foldable, and yet determinants of foldability may be inapparent once the native state is reached. Insight has emerged from studies of diseases of protein misfolding, exemplified by monogenic diabetes mellitus due to mutations in proinsulin leading to endoplasmic reticulum stress and β-cell death. Cellular foldability of human proinsulin requires an invariant Phe within a conserved crevice at the receptor-binding surface (position B24).

Mesenchymal stem cells in experimental autoimmune encephalomyelitis model of multiple sclerosis: A systematic review and meta-analysis.

Background/aims: Mesenchymal stem cells (MSCs) transplantation has been considered a possible therapeutic method for Multiple Sclerosis (MS). However, no quantitative data synthesis of MSCs therapy for MS exists. We conducted a systematic review and meta-analysis to evaluate the effects of MSCs in experimental autoimmune encephalomyelitis (EAE) animal model of MS.

"Register-shift" insulin analogs uncover constraints of proteotoxicity in protein evolution.

Globular protein sequences encode not only functional structures (the native state) but also protein foldability, a conformational search that is both efficient and robustly minimizes misfolding. Studies of mutations associated with toxic misfolding have yielded insights into molecular determinants of protein foldability. Of particular interest are residues that are conserved yet dispensable in the native state.