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. To address this issue, we propose an accelerated denoising diffusion probabilistic model via truncated inverse processes (ADDPM) that is specifically designed for medical image segmentation. The inverse process of ADDPM starts from a non-Gaussian distribution and terminates early once a prediction with relatively low noise is obtained after multiple iterations of denoising. We employ a separate powerful segmentation network to obtain pre-segmentation and construct the non-Gaussian distribution of the segmentation based on the forward diffusion rule. By further adopting a separate denoising network, the final segmentation can be obtained with just one denoising step from the predictions with low noise. ADDPM greatly reduces the number of denoising steps to approximately one-tenth of that in vanilla DDPMs. Our experiments on four segmentation tasks demonstrate that ADDPM outperforms both vanilla DDPMs and existing representative accelerating DDPMs methods. Moreover, ADDPM can be easily integrated with existing advanced segmentation models to improve segmentation performance and provide uncertainty estimation. Implementation code: https://github.com/Guoxt/ADDPM.

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. In this study, we aim to bridge this gap. In particular, (1) we curated a comprehensive dataset by collating images from 30 datasets, which comprises 70,781 samples of 46,686 participants. Moreover, we introduce pseudo-functional connectivity (pFC) to further generates millions of augmented brain networks by randomly dropping certain timepoints of the BOLD signal. (2) We propose the BrainMass framework for brain network self-supervised learning via mask modeling and feature alignment. BrainMass employs Mask-ROI Modeling (MRM) to bolster intra-network dependencies and regional specificity. Furthermore, Latent Representation Alignment (LRA) module is utilized to regularize augmented brain networks of the same participant with similar topological properties to yield similar latent representations by aligning their latent embeddings. Extensive experiments on eight internal tasks and seven external brain disorder diagnosis tasks show BrainMass's superior performance, highlighting its significant generalizability and adaptability. Nonetheless, BrainMass demonstrates powerful few/zero-shot learning abilities and exhibits meaningful interpretation to various diseases, showcasing its potential use for clinical applications.

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

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. Here, we describe an alternative approach that exploits an endogenous glucose-dependent switch in hepatic physiology: preferential insulin signaling (under hyperglycemic conditions) versus preferential counter-regulatory glucagon signaling (during hypoglycemia). Motivated by prior reports of glucagon-insulin co-infusion, we designed and tested an ultra-stable glucagon-insulin fusion protein whose relative hormonal activities were calibrated by respective modifications; physical stability was concurrently augmented to facilitate formulation, enhance shelf life and expand access. An N-terminal glucagon moiety was stabilized by an α-helix-compatible Lys 13 -Glu 17 lactam bridge; A C-terminal insulin moiety was stabilized as a single chain with foreshortened C domain. Studies in vitro demonstrated (a) resistance to fibrillation on prolonged agitation at 37 °C and (b) dual hormonal signaling activities with appropriate balance. Glucodynamic responses were monitored in rats relative to control fusion proteins lacking one or the other hormonal activity, and continuous intravenous infusion emulated basal subcutaneous therapy. Whereas efficacy in mitigating hyperglycemia was unaffected by the glucagon moiety, the fusion protein enhanced endogenous glucose production under hypoglycemic conditions. Together, these findings provide proof of principle toward a basal glucose-responsive insulin biotechnology of striking simplicity. The fusion protein's augmented stability promises to circumvent the costly cold chain presently constraining global insulin access. Significance Statement: The therapeutic goal of insulin replacement therapy in diabetes is normalization of blood-glucose concentration, which prevents or delays long-term complications. A critical barrier is posed by recurrent hypoglycemic events that results in short- and long-term morbidities. An innovative approach envisions co-injection of glucagon (a counter-regulatory hormone) to exploit a glycemia-dependent hepatic switch in relative hormone responsiveness. To provide an enabling technology, we describe an ultra-stable fusion protein containing insulin- and glucagon moieties. Proof of principle was obtained in rats. A single-chain insulin moiety provides glycemic control whereas a lactam-stabilized glucagon extension mitigates hypoglycemia. This dual-hormone fusion protein promises to provide a basal formulation with reduced risk of hypoglycemia. Resistance to fibrillation may circumvent the cold chain required for global access.

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). Unfortunately, the accuracy of PLs may deteriorate due to domain shift, thus limiting the effectiveness of the adaptation process. To address this issue, we propose a Chebyshev confidence guided SFDA framework to accurately assess the reliability of PLs and generate self-improving PLs for self-training. The Chebyshev confidence is estimated by calculating the probability lower bound of PL confidence, given the prediction and the corresponding uncertainty. Leveraging the Chebyshev confidence, we introduce two confidence-guided denoising methods: direct denoising and prototypical denoising. Additionally, we propose a novel teacher-student joint training scheme (TJTS) that incorporates a confidence weighting module to iteratively improve PLs' accuracy. The TJTS, in collaboration with the denoising methods, effectively prevents the propagation of noise and enhances the accuracy of PLs. Extensive experiments in diverse domain scenarios validate the effectiveness of our proposed framework and establish its superiority over state-of-the-art SFDA methods. Our paper contributes to the field of SFDA by providing a novel approach for precisely estimating the reliability of PLs and a framework for obtaining high-quality PLs, resulting in improved adaptation performance.

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., functional and structural connectivity derived from fMRI and DTI, are in some manner interacted but are not necessarily linearly related. Accordingly, there remains a great challenge to leverage complementary information for brain connectome analysis. Recently, Graph Convolutional Networks (GNN) have been widely applied to the fusion of multi-modal brain connectome. However, most existing GNN methods fail to couple inter-modal relationships. In this regard, we propose a Cross-modal Graph Neural Network (Cross-GNN) that captures inter-modal dependencies through dynamic graph learning and mutual learning. Specifically, the inter-modal representations are attentively coupled into a compositional space for reasoning inter-modal dependencies. Additionally, we investigate mutual learning in explicit and implicit ways: (1) Cross-modal representations are obtained by cross-embedding explicitly based on the inter-modal correspondence matrix. (2) We propose a cross-modal distillation method to implicitly regularize latent representations with cross-modal semantic contexts. We carry out statistical analysis on the attentively learned correspondence matrices to evaluate inter-modal relationships for associating disease biomarkers. Our extensive experiments on three datasets demonstrate the superiority of our proposed method for disease diagnosis with promising prediction performance and multi-modal connectome biomarker location.

Evaluating the resource curse hypothesis and the interplay of financial development, human development, and political stability in seven emerging economies.

The present study empirically confabulates the authenticity of the "resource curse hypothesis" in selected emerging nations. Furthermore, we also assessed the interconnections of three essential economic indicators with financial development, i.e., human development, political stability, and gross domestic product. To effectuate these objectives, we used annual data for the time frame 1990 to 2020 and advanced panel estimation techniques for getting the empirical outcomes. The study's empirical outcomes illustrate the existence of the "resource curse hypothesis" in sample nations. In addition, human development index and gross domestic product play an essential part in the furtherance of financial development in the long-run. The human development index is upsurging the financial development. Furthermore, political stability is also exerting a favorable influence on financial development. A similar interconnection is observed in the short-time period; nonetheless, the amplitude of the short-run impacts is smaller if we have a look at the long-run impacts. The empirical analysis offers a few pertinent policy insights for policymakers to improve the situation in the selected sample. Note: Financial development positively interconnected with human development, GDP and political stability while negatively associated with natural resources, respectively.

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. In particular, we propose a Confidence Regularized Knowledge Distillation (CReg-KD) framework, which adaptively transfers knowledge for distillation in light of knowledge confidence. Two strategies are advocated to regularize the global and local dependencies between teacher and student knowledge. In detail, a gated distillation mechanism is proposed to soften the transferred knowledge globally by utilizing the teacher loss as a confidence score. Moreover, the intermediate representations are attentively and locally refined with key semantic context to mimic meaningful features. To demonstrate the superiority of our proposed framework, we evaluated the framework on two brain imaging analysis tasks (i.e. Alzheimer's Disease classification and brain age estimation based on T1-weighted MRI) on the Alzheimer's Disease Neuroimaging Initiative dataset including 902 subjects and a cohort of 3655 subjects from 4 public datasets. Extensive experimental results show that CReg-KD achieves consistent improvements over the baseline teacher model and outperforms other state-of-the-art knowledge distillation approaches, manifesting that CReg-KD as a powerful medical image analysis tool in terms of both promising prediction performance and generalizability.

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 I2 test. Associations of sarcopenia with mortality of post-TAVI were expressed as hazard ratio (HR) or odds ratios (OR) and 95% CI. RESULTS: 13 studies involving 5248 patients (mean age from 78.1 to 84.9 years) undergoing TAVI were included. There were eleven studies defined sarcopenia based on loss of skeletal muscle mass index (SMI), while only two studies used low muscle mass plus low muscle strength and/or low physical performance. Overall, the pooled prevalence of sarcopenia in patients undergoing TAVI was 49% (95% CI 41%-58%). Sarcopenia was associated with an increased risk of long-term (≥1 year) mortality in patients after TAVI (HR 1.57, 95% CI 1.33-1.85, P < 0.001), with similar findings in the subgroups stratified by follow-up time, definition of sarcopenia, study location, and study design. Furthermore, the 1-, 2-, and 3-year cumulative probabilities of survival in patients with sarcopenia were significantly lower than non-sarcopenia (74.0% vs 91.0%, 68.3% vs 78.0%, and 72.6% vs 79.8%, all P < 0.05). CONCLUSIONS: Although there are substantial differences in diagnostic criteria, sarcopenia is highly prevalent in patients undergoing TAVI and its linked to increased long-term mortality after TAVI.

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. In addition, the relative risk (RR)/odds ratio (OR) or standardized mean difference (SMD) and 95 % confidence interval (CI) were calculated. RESULTS: Finally, a total of 34 RCTs including 3758 patients were included in this meta-analysis. There were 5 articles that reported hydropathic compress with astrogalin, 14 articles that reported MEBO burn cream, 9 articles that reported compound cortex phellodendri liquid and 6 articles that reported Shengji Yuhong ointment. Compared with the basic treatment, the externally applied TCM (astrogalin, MEBO burn cream, compound cortex phellodendri liquid and Shengji Yuhong ointment) combined with basic treatment improved the total effective rate (RR = 1.31 [1.20, 1.42], P < 0.0001) and healing rate (RR = 1.84 [1.56, 2.17], P < 0.0001) and shortened the healing time (SMD = - 2.51 [- 3.39, - 1.63], P < 0.0001). CONCLUSION: Our systematic review and meta-analysis revealed that common TCM applied externally could significantly improve the clinical efficacy comparing to the basic treatment.

Prognostic value of temporal muscle thickness, a novel radiographic marker of sarcopenia, in patients with brain tumor: A systematic review and meta-analysis.

Sarcopenia has been identified as a prognostic factor among certain types of cancer. However, it is unclear whether there is prognostic value of temporalis muscle thickness (TMT), a potential surrogate for sarcopenia, in adults patients with brain tumors. Therefore, we searched the Medline, Embase, and PubMed to systematically review and meta-analyze the relationship between TMT and overall survival, progression-free survival, and complications in patients with brain tumors and the hazard ratio (HR) or odds ratios (OR), and 95% confidence interval (CI) were evaluated. The quality in prognostic studies (QUIPS) instrument was employed to evaluate study quality. Nineteen studies involving 4570 patients with brain tumors were included for qualitative and quantitative analysis. Meta-analysis revealed thinner TMT was associated with poor overall survival (HR, 1.72; 95% CI, 1.45-2.04; P < 0.01) in patients with brain tumors. Sub-analyses showed that the association existed for both primary brain tumors (HR, 2.02; 95% CI, 1.55-2.63) and brain metastases (HR, 1.39; 95% CI, 1.30-1.49). Moreover, thinner TMT also was the independent predictor of progression-free survival in patients with primary brain tumors (HR, 2.88; 95% CI, 1.85-4.46; P < 0.01). Therefore, to improve clinical decision making it is important to integrate TMT assessment into routine clinical settings in patients with brain tumors.

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). Here, we propose a multivariate distance-based connectome network (MDCN) that addresses the local specificity problem by efficient parcellation-wise learning, as well as associating population and parcellation dependencies to map individual differences. The approach incorporating an explainable method, parcellation-wise gradient and class activation map (p-GradCAM), is feasible for identifying individual patterns of interest and pinpointing connectome associations with diseases. We demonstrate the utility of our method on two largely aggregated multicenter public datasets by distinguishing ASD and ADHD from healthy controls and assessing their associations with underlying diseases. Extensive experiments have demonstrated the superiority of MDCN in classification and interpretation, where MDCN outperformed competitive state-of-the-art methods and achieved a high proportion of overlap with previous findings. As a CWAS-guided deep learning method, our proposed MDCN framework may narrow the bridge between deep learning and CWAS approaches, and provide new insights for connectome-wide association studies.

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. Although not contacting DNA, the aromatic ring seals the domain's minor wing at a solvent-exposed junction with a basic tail. A seeming paradox was posed by the native-like biochemical properties of inherited Swyer variant Y72F: its near-native gene-regulatory activity is consistent with the father's male development, but at odds with the daughter's XY female somatic phenotype. Surprisingly, aromatic rings (Y72, F72 or W72) confer higher transcriptional activity than do basic or polar side chains generally observed at solvated DNA interfaces (Arg, Lys, His or Gln). Whereas biophysical studies (time-resolved fluorescence resonance energy transfer and heteronuclear NMR spectroscopy) uncovered only subtle perturbations, dissociation of the Y72F complex was markedly accelerated relative to wild-type. Studies of protein-DNA solvation by molecular-dynamics (MD) simulations of an homologous high-resolution crystal structure (SOX18) suggest that Y72 para-OH anchors a network of water molecules at the tail-DNA interface, perturbed in the variant in association with nonlocal conformational fluctuations. Loss of the Y72 anchor among SRY variants presumably "unclamps" its basic tail, leading to (a) rapid DNA dissociation despite native affinity and (b) attenuated transcriptional activity at the edge of sexual ambiguity. Conservation of Y72 suggests that this water-mediated clamp operates generally among SRY and metazoan SOX domains.

Ensemble learning via 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. However, multiple annotations are often not available in a real-world scenario. To mitigate the issue, this paper proposes a supervision augmentation method (SA) and combines it with ensemble learning (SA-EN) to improve the generalization ability of the model. SA can obtain diverse supervision information by estimating the uncertainty of annotation in a real-world scenario that per image have only one ambiguous annotation. Then different base learners in EN are trained with diverse supervision information. The experimental results on two white matter hyperintensity segmentation datasets demonstrate that SA-EN gets the optimal accuracy compared with other state-of-the-art ensemble methods. SA-EN is more effective on small datasets, which is more suitable for medical image segmentation with few annotations. A quantitative study is presented to show the effect of ensemble size and the effectiveness of the ensemble model. Furthermore, SA-EN can capture two types of uncertainty, aleatoric uncertainty modeled in SA and epistemic uncertainty modeled in EN.

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. Specifically, we regressed age with volumetric brain features using machine learning to construct the brain age model, and model-agnostic Shapley values were calculated to attribute regional brain aging for each subject's age estimation, forming the brain age vector. Spatial specificity of the brain age vector was evaluated among groups of normal aging, prodromal Parkinson disease (PD), stable mild cognitive impairment (sMCI), and progressive mild cognitive impairment (pMCI). Machine learning methods were adopted to examine the discriminability of the brain age vector in early disease screening, compared with the other two brain aging metrics (single brain age gap, regional brain age gaps) and brain volumes. Results showed that the proposed brain age vector accurately reflected disorder-specific abnormal aging patterns related to the medial temporal and the striatum for prodromal AD (sMCI vs. pMCI) and PD (healthy controls [HC] vs. prodromal PD), respectively, and demonstrated outstanding performance in early disease screening, with area under the curves of 83.39% and 72.28% in detecting pMCI and prodromal PD, respectively. In conclusion, the proposed brain age vector effectively improves spatial specificity of brain aging measurement and enables individual screening of neurodegenerative diseases.

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. Traditional neuroimaging analysis pipelines are implemented based on registration methods, which involve time-consuming optimization steps. Recent related deep learning methods speed up the segmentation pipeline but are limited to distinguishing fuzzy boundaries, especially encountering the multi-grained whole-brain segmentation task, where there exists high variability in size and shape among various anatomical regions. In this article, we propose a deep learning-based network, termed Multi-branch Residual Fusion Network, for the whole brain segmentation, which is capable of segmenting the whole brain into 136 parcels in seconds, outperforming the existing state-of-the-art networks. To tackle the multi-grained regions, the multi-branch cross-attention module (MCAM) is proposed to relate and aggregate the dependencies among multi-grained contextual information. Moreover, we propose a residual error fusion module (REFM) to improve the network's representations fuzzy boundaries. Evaluations of two datasets demonstrate the reliability and generalization ability of our method for the whole brain segmentation, indicating that our method represents a rapid and efficient segmentation tool for neuroimage analysis.

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

Toxic misfolding of proinsulin variants in ß-cells defines a monogenic diabetes syndrome, designated mutant INS-gene induced diabetes of the young (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 (LeuB15→Pro, LeuA16→Pro, and PheB24→Ser) probe residues conserved among vertebrate insulins. In this companion study, we describe 1H and 1H-13C NMR studies of the peptides; key NMR resonance assignments were verified by synthetic 13C-labeling. Parent spectra retain nativelike features in the neighborhood of the single disulfide bridge (cystine B19-A20), including secondary NMR chemical shifts and nonlocal nuclear Overhauser effects. This partial fold engages wild-type side chains LeuB15, LeuA16 and PheB24 at the nexus of nativelike α-helices α1 and α3 (as defined in native proinsulin) and flanking ß-strand (residues B24-B26). The variant peptides exhibit successive structural perturbations in order: parent (most organized) > SerB24 >> ProA16 > ProB15 (least organized). The same order pertains to (a) overall α-helix content as probed by circular dichroism, (b) synthetic yields of corresponding three-disulfide insulin analogs, and (c) ER stress induced in cell culture by corresponding mutant proinsulins. These findings suggest that this and related peptide models will provide a general platform for classification of MIDY mutations based on molecular mechanisms by which nascent disulfide pairing is impaired. We propose that the syndrome's variable phenotypic spectrum-onsets ranging from the neonatal period to later in childhood or adolescence-reflects structural features of respective folding intermediates.

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 mutant INS-gene induced diabetes of the young (MIDY), this syndrome defines molecular determinants of foldability in the endoplasmic reticulum (ER) of ß-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 (LeuB15→Pro, LeuA16→Pro, and PheB24→Ser). The studies exploited a 49-residue single-chain synthetic precursor (designated DesDi), previously shown to optimize in vitro efficiency of disulfide pairing. Parent and variant peptides contain a single disulfide bridge (cystine B19-A20) to provide a model of proinsulin's first oxidative folding intermediate. The peptides were characterized by circular dichroism and redox stability in relation to effects of the mutations on (a) in vitro foldability of the corresponding insulin analogs and (b) ER stress induced in cell culture on expression of the corresponding variant proinsulins. Striking correlations were observed between peptide biophysical properties, degree of ER stress and age of diabetes onset (neonatal or adolescent). Our findings suggest that age of onset reflects the extent to which nascent structure is destabilized in proinsulin's putative folding nucleus. We envisage that such peptide models will enable high-resolution structural studies of key folding determinants and in turn permit molecular dissection of phenotype-genotype relationships in this monogenic diabetes syndrome. Our companion study (next article in this issue) employs two-dimensional heteronuclear NMR spectroscopy to define site-specific perturbations in the variant peptides.

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. This scheme exploited a diol sensor (meta-fluoro-phenylboronic acid at GlyA1) and internal diol (3,4-dihydroxybenzoate at LysB28). The sensor recognizes monosaccharides (fructose > glucose). Studies of insulin-signaling in human hepatoma-derived cells (HepG2) demonstrated fructose-dependent receptor autophosphorylation leading to appropriate downstream signaling events, including a specific kinase cascade and metabolic gene regulation (gluconeogenesis and lipogenesis). Addition of glucose (an isomeric ligand with negligible sensor affinity) did not activate the hormone. Similarly, metabolite-regulated signaling was not observed in control studies of 1) an unmodified insulin analog or 2) an analog containing a diol sensor without internal tethering. Although secondary structure (as probed by circular dichroism) was unaffected by ligand-binding, heteronuclear NMR studies revealed subtle local and nonlocal monosaccharide-dependent changes in structure. Insertion of a synthetic switch into insulin has thus demonstrated coupling between hinge-opening and allosteric holoreceptor signaling. In addition to this foundational finding, our results provide proof of principle for design of a mechanism-based metabolite-responsive insulin. In particular, replacement of the present fructose sensor by an analogous glucose sensor may enable translational development of a "smart" insulin analog to mitigate hypoglycemic risk in diabetes therapy.

Alteration of brain structural connectivity in progression of Parkinson's disease: A connectome-wide network analysis.

Pinpointing the brain dysconnectivity in idiopathic rapid eye movement sleep behaviour disorder (iRBD) can facilitate preventing the conversion of Parkinson's disease (PD) from prodromal phase. Recent neuroimage investigations reported disruptive brain white matter connectivity in both iRBD and PD, respectively. However, the intrinsic process of the human brain structural network evolving from iRBD to PD still remains largely unknown. To address this issue, 151 participants including iRBD, PD and age-matched normal controls were recruited to receive diffusion MRI scans and neuropsychological examinations. The connectome-wide association analysis was performed to detect reorganization of brain structural network along with PD progression. Eight brain seed regions in both cortical and subcortical areas demonstrated significant structural pattern changes along with the progression of PD. Applying machine learning on the key connectivity related to these seed regions demonstrated better classification accuracy compared to conventional network-based statistic. Our study shows that connectome-wide association analysis reveals the underlying structural connectivity patterns related to the progression of PD, and provide a promising distinct capability to predict prodromal PD patients.

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. Based on the stable configurations of the ground states, the electronic structure property is analyzed systematically. The results show that these macrocyclic molecules have excellent electron transport capability and electrochemical activity. Then, the electron absorption spectra of each system are carried out by using time-dependent density functional theory (TD-DFT) at the M062X/6-311+G(d) level. It turns out that their maximum absorption wavelengths are all in the visible range. Further calculation suggests that C[n]QTTF can also be characterized with one-dimensional self-assembly, double-walled assembly, and the host-guest inclusion performance, based on which it gains a variety of supramolecular structures. In summary, the benzoquinone-tetrafluorofurene macrocyclic molecules predicted by DFT calculations may be of assistance to the potential applications in organic electronics and supramolecular chemistry.