Social network-based approaches to HIV testing: a systematic review and meta-analysis.

INTRODUCTION: Social network-based testing approaches (SNAs) encourage individuals ("test promoters") to motivate sexual partners and/or those in their social networks to test for HIV. We conducted a systematic review to examine the effectiveness, acceptability and cost-effectiveness of SNA. METHODS: We searched five databases from January 2010 to May 2023, and included studies that compared SNA with non-SNA. We used random-effects meta-analysis to combine effect estimates. Certainty was assessed using the GRADE approach. RESULTS: We identified 47 studies. SNA may increase uptake of HIV testing compared to non-SNA (RR 2.04, 95% CI: 1.06-3.95, Low certainty). The proportion of first-time testers was probably higher among partners or social contacts of test promoters using SNA compared to non-SNA (RR 1.49, 95% CI: 1.22-1.81, Moderate certainty). The proportion of people who tested positive for HIV may be higher among partners or social contacts of test promoters using SNA compared to non-SNA (RR 1.84, 95% CI: 1.01-3.35, Low certainty). There were no reports of any adverse events or harms associated with SNA. Based on six cost-effectiveness studies, SNA was generally cheaper per person tested and per person diagnosed compared to non-SNA. Based on 23 qualitative studies, SNA is likely to be acceptable to a variety of populations. DISCUSSION: Our review collated evidence for SNA to HIV testing covering the key populations and the general population who may benefit from HIV testing. We summarized evidence for the effectiveness, acceptability and cost-effectiveness of different models of SNA. While we did not identify an ideal model of SNA that could be immediately scaled up, for each setting and population targeted, we recommend various implementation considerations as our meta-analysis showed the effectiveness might differ due to factors which include the testing modality (i.e. use of HIV self-testing), type of test promoters, long or short duration of recruitment and use of financial incentives. CONCLUSIONS: Social network-based approaches may enhance HIV testing uptake, increase the proportion of first-time testers and those testing positive for HIV. Heterogeneity among studies highlights the need for context-specific adaptations, but the overall positive impact of SNA on HIV testing outcomes could support its integration into existing HIV testing services.

Aberrant high-beta band functional connectivity during reward processing in melancholic major depressive disorder: An MEG study.

OBJECTIVE: To identify the spatial-temporal pattern variation of whole-brain functional connectivity (FC) during reward processing in melancholic major depressive disorder (MDD) patients, and to determine the clinical correlates of connectomic differences. METHODS: 61 MDD patients and 32 healthy controls were enrolled into the study. During magnetoencephalography (MEG) scanning, all participants completed the facial emotion recognition task. The MDD patients were further divided into two groups: melancholic (n = 31) and non-melancholic (n = 30), based on the Mini International Neuropsychiatric Interview (M.I.N.I.) assessment. Melancholic symptoms were examined by using the 6-item melancholia subscale from the Hamilton Depression Rating Scale (HAM-D6). The whole-brain orthogonalized power envelope connections in the high-beta band (20-35 Hz) were constructed in each period after the happy emotional stimuli (0-200 ms, 100-300 ms, 200-400 ms, 300-500 ms, and 400-600 ms). Then, the network-based statistic (NBS) was used to determine the specific abnormal connection patterns in melancholic MDD patients. RESULTS: The NBS identified a sub-network difference at the mid-late period (300-500 ms) in response to happy faces among the three groups (corrected P = 0.035). Then, the post hoc and correlation analyses found five FCs were decreased in melancholic MDD patients and were related to HAM-D6 score, including FCs of left fusiform gyrus-right orbital inferior frontal gyrus (r = -0.52, P < 0.001), left fusiform gyrus-left amygdala (r = -0.26, P = 0.049), left posterior cingulate gyrus-right precuneus (r = -0.32, P = 0.025), left precuneus-right precuneus (r = -0.27, P = 0.049), and left precuneus-left inferior occipital gyrus (r = -0.32, P = 0.025). CONCLUSION: In response to happy faces, melancholic MDD patients demonstrated a disrupted functional connective pattern (20-35 Hz, 300-500 ms), which involved brain regions in visual information processing and the limbic system. The aberrant functional connective pattern in reward processing might be a biomarker of melancholic MDD.

Joint learning of nonlinear representation and projection for fast constrained MRSI reconstruction.

PURPOSE: To develop and evaluate a novel method for computationally efficient reconstruction from noisy MR spectroscopic imaging (MRSI) data. METHODS: The proposed method features (a) a novel strategy that jointly learns a nonlinear low-dimensional representation of high-dimensional spectroscopic signals and a neural-network-based projector to recover the low-dimensional embeddings from noisy/limited data; (b) a formulation that integrates the forward encoding model, a regularizer exploiting the learned representation, and a complementary spatial constraint; and (c) a highly efficient algorithm enabled by the learned projector within an alternating direction method of multipliers (ADMM) framework, circumventing the computationally expensive network inversion subproblem. RESULTS: The proposed method has been evaluated using simulations as well as in vivo 1 $$ {}^1 $$ H and 31 $$ {}^{31} $$ P MRSI data, demonstrating improved performance over state-of-the-art methods, with about 6 × $$ \times $$ fewer averages needed than standard Fourier reconstruction for similar metabolite estimation variances and up to 100 × $$ \times $$ reduction in processing time compared to a prior neural network constrained reconstruction method. Computational and theoretical analyses were performed to offer further insights into the effectiveness of the proposed method. CONCLUSION: A novel method was developed for fast, high-SNR spatiospectral reconstruction from noisy MRSI data. We expect our method to be useful for enhancing the quality of MRSI or other high-dimensional spatiospectral imaging data.

The impact of insomnia on brain networks topology in depressed patients: A resting-state fMRI study.

OBJECTIVE: Depression and insomnia frequently co-occur, but the neural mechanisms between patients with varying degrees of these conditions are not fully understood. The specific topological features and connectivity patterns of this co-morbidity have not been extensively studied. This study aimed to investigate the topological characteristics of topological characteristics and functional connectivity of brain networks in depressed patients with insomnia. METHODS: Resting-state functional magnetic resonance imaging data from 32 depressed patients with a high level of insomnia (D-HI), 35 depressed patients with a low level of insomnia (D-LI), and 81 healthy controls (HC) were used to investigate alterations in brain topological organization functional networks. Nodal and global properties were analyzed using graph-theoretic techniques, and network-based statistical analysis was employed to identify changes in brain network functional connectivity. RESULTS: Compared to the HC group, both the D-HI and D-LI groups showed an increase in the global efficiency (Eglob) values, local efficiency (Eloc) was decreased in the D-HI group, and Lambda and shortest path length (Lp) values were decreased in the D-LI group. At the nodal level, the right parietal nodal clustering coefficient (NCp) values were reduced in D-HI and D-LI groups compared to those in HC. The functional connectivity of brain networks in patients with D-HI mainly involves default mode network (DMN)-cingulo-opercular network (CON), DMN-visual network (VN), DMN-sensorimotor network (SMN), and DMN-cerebellar network (CN), while that in patients with D-LI mainly involves SMN-CON, SMN-SMN, SMN-VN, and SMN-CN. The values of the connection between the midinsula and postoccipital gyrus was negatively correlated with scores for early awakening in D-HI. CONCLUSION: These findings may contribute to our understanding of the underlying neuropsychological mechanisms in depressed patients with insomnia.

Insertable Glucose Sensor Using a Compact and Cost-Effective Phosphorescence Lifetime Imager and Machine Learning.

Optical continuous glucose monitoring (CGM) systems are emerging for personalized glucose management owing to their lower cost and prolonged durability compared to conventional electrochemical CGMs. Here, we report a computational CGM system, which integrates a biocompatible phosphorescence-based insertable biosensor and a custom-designed phosphorescence lifetime imager (PLI). This compact and cost-effective PLI is designed to capture phosphorescence lifetime images of an insertable sensor through the skin, where the lifetime of the emitted phosphorescence signal is modulated by the local concentration of glucose. Because this phosphorescence signal has a very long lifetime compared to tissue autofluorescence or excitation leakage processes, it completely bypasses these noise sources by measuring the sensor emission over several tens of microseconds after the excitation light is turned off. The lifetime images acquired through the skin are processed by neural network-based models for misalignment-tolerant inference of glucose levels, accurately revealing normal, low (hypoglycemia) and high (hyperglycemia) concentration ranges. Using a 1 mm thick skin phantom mimicking the optical properties of human skin, we performed in vitro testing of the PLI using glucose-spiked samples, yielding 88.8% inference accuracy, also showing resilience to random and unknown misalignments within a lateral distance of ∼4.7 mm with respect to the position of the insertable sensor underneath the skin phantom. Furthermore, the PLI accurately identified larger lateral misalignments beyond 5 mm, prompting user intervention for realignment. The misalignment-resilient glucose concentration inference capability of this compact and cost-effective PLI makes it an appealing wearable diagnostics tool for real-time tracking of glucose and other biomarkers.

Alzheimer's Disease Prediction Using Fly-Optimized Densely Connected Convolution Neural Networks Based on MRI Images.

Alzheimer's is a degenerative brain cell disease that affects around 5.8 million people globally. The progressive neurodegenerative disease known as Alzheimer's Disease (AD), affects the frontal cortex, the part of the brain in charge of memory, language, and cognition. As a result, researchers are utilizing a variety of machine-learning techniques to create an automated method for AD detection. The massive data collected during ROI and biomarker identification takes longer to handle using current methods. This study uses metaheuristic-tuned deep learning to detect the AD-affected region. The research utilizes advanced deep learning and image processing techniques to enhance early and accurate diagnosis of Alzheimer's disease, potentially enhancing patient outcomes and prompt therapy. The capacity of deep neural networks to extract complex patterns from magnetic resonance imaging (MRI) scans makes them indispensable in the diagnosis of AD since they allow the detection of minor aberrations and complex alterations in brain structure and composition. An adaptive histogram approach processes the collected photographs, and a weighted median filter is used in place of the noisy pixels. The next step is to identify the issue region using a deep convolution network-based clustering segmentation process. A correlated information theory approach is used to extract various textural and statistical features from the separated regions. Lastly, the selected features are probed by the fly-optimized densely linked convolution neural networks. The method surpasses state-of-the-art techniques in sensitivity (15.52%), specificity (15.62%), accuracy (9.01%), error rate (11.29%), and F-measure (10.52%) for recognizing AD-impacted regions in MRI scans using the Kaggle dataset.

Analyzing the topological properties of resting-state brain function network connectivity based on graph theoretical methods in patients with high myopia.

AIM: Recent imaging studies have found significant abnormalities in the brain's functional or structural connectivity among patients with high myopia (HM), indicating a heightened risk of cognitive impairment and other behavioral changes. However, there is a lack of research on the topological characteristics and connectivity changes of the functional networks in HM patients. In this study, we employed graph theoretical analysis to investigate the topological structure and regional connectivity of the brain function network in HM patients. METHODS: We conducted rs-fMRI scans on 82 individuals with HM and 59 healthy controls (HC), ensuring that the two groups were matched for age and education level. Through graph theoretical analysis, we studied the topological structure of whole-brain functional networks among participants, exploring the topological properties and differences between the two groups. RESULTS: In the range of 0.05 to 0.50 of sparsity, both groups demonstrated a small-world architecture of the brain network. Compared to the control group, HM patients showed significantly lower values of normalized clustering coefficient (γ) (P = 0.0101) and small-worldness (σ) (P = 0.0168). Additionally, the HM group showed lower nodal centrality in the right Amygdala (P < 0.001, Bonferroni-corrected). Notably, there is an increase in functional connectivity (FC) between the saliency network (SN) and Sensorimotor Network (SMN) in the HM group, while the strength of FC between the basal ganglia is relatively weaker (P < 0.01). CONCLUSION: HM Patients exhibit reduced small-world characteristics in their brain networks, with significant drops in γ and σ values indicating weakened global interregional information transfer ability. Not only that, the topological properties of the amygdala nodes in HM patients significantly decline, indicating dysfunction within the brain network. In addition, there are abnormalities in the FC between the SN, SMN, and basal ganglia networks in HM patients, which is related to attention regulation, motor impairment, emotions, and cognitive performance. These findings may provide a new mechanism for central pathology in HM patients.

Herb-CMap: a multimodal fusion framework for deciphering the mechanisms of action in traditional Chinese medicine using Suhuang antitussive capsule as a case study.

Herbal medicines, particularly traditional Chinese medicines (TCMs), are a rich source of natural products with significant therapeutic potential. However, understanding their mechanisms of action is challenging due to the complexity of their multi-ingredient compositions. We introduced Herb-CMap, a multimodal fusion framework leveraging protein-protein interactions and herb-perturbed gene expression signatures. Utilizing a network-based heat diffusion algorithm, Herb-CMap creates a connectivity map linking herb perturbations to their therapeutic targets, thereby facilitating the prioritization of active ingredients. As a case study, we applied Herb-CMap to Suhuang antitussive capsule (Suhuang), a TCM formula used for treating cough variant asthma (CVA). Using in vivo rat models, our analysis established the transcriptomic signatures of Suhuang and identified its key compounds, such as quercetin and luteolin, and their target genes, including IL17A, PIK3CB, PIK3CD, AKT1, and TNF. These drug-target interactions inhibit the IL-17 signaling pathway and deactivate PI3K, AKT, and NF-κB, effectively reducing lung inflammation and alleviating CVA. The study demonstrates the efficacy of Herb-CMap in elucidating the molecular mechanisms of herbal medicines, offering valuable insights for advancing drug discovery in TCM.

A comprehensive review and comparison of existing computational methods for protein function prediction.

Protein function prediction is critical for understanding the cellular physiological and biochemical processes, and it opens up new possibilities for advancements in fields such as disease research and drug discovery. During the past decades, with the exponential growth of protein sequence data, many computational methods for predicting protein function have been proposed. Therefore, a systematic review and comparison of these methods are necessary. In this study, we divide these methods into four different categories, including sequence-based methods, 3D structure-based methods, PPI network-based methods and hybrid information-based methods. Furthermore, their advantages and disadvantages are discussed, and then their performance is comprehensively evaluated and compared. Finally, we discuss the challenges and opportunities present in this field.

Systematic exploration of therapeutic effects and key mechanisms of Panax ginseng using network-based approaches.

Background: Network pharmacology has emerged as a powerful tool to understand the therapeutic effects and mechanisms of natural products. However, there is a lack of comprehensive evaluations of network-based approaches for natural products on identifying therapeutic effects and key mechanisms. Purpose: We systematically explore the capabilities of network-based approaches on natural products, using Panax ginseng as a case study. P. ginseng is a widely used herb with a variety of therapeutic benefits, but its active ingredients and mechanisms of action on chronic diseases are not yet fully understood. Methods: Our study compiled and constructed a network focusing on P. ginseng by collecting and integrating data on ingredients, protein targets, and known indications. We then evaluated the performance of different network-based methods for summarizing known and unknown disease associations. The predicted results were validated in the hepatic stellate cell model. Results: We find that our multiscale interaction-based approach achieved an AUROC of 0.697 and an AUPR of 0.026, which outperforms other network-based approaches. As a case study, we further tested the ability of multiscale interactome-based approaches to identify active ingredients and their plausible mechanisms for breast cancer and liver cirrhosis. We also validated the beneficial effects of unreported and top-predicted ingredients, in cases of liver cirrhosis and gastrointestinal neoplasms. Conclusion: our study provides a promising framework to systematically explore the therapeutic effects and key mechanisms of natural products, and highlights the potential of network-based approaches in natural product research.

Meta-learning based blind image super-resolution approach to different degradations.

Although recent studies on blind single image super-resolution (SISR) have achieved significant success, most of them typically require supervised training on synthetic low resolution (LR)-high resolution (HR) paired images. This leads to re-training necessity for different degradations and restricted applications in real-world scenarios with unfavorable inputs. In this paper, we propose an unsupervised blind SISR method with input underlying different degradations, named different degradations blind super-resolution (DDSR). It formulates a Gaussian modeling on blur degradation and employs a meta-learning framework for solving different image degradations. Specifically, a neural network-based kernel generator is optimized by learning from random kernel samples, referred to as random kernel learning. This operation provides effective initialization for blur degradation optimization. At the same time, a meta-learning framework is proposed to resolve multiple degradation modelings on the basis of alternative optimization between blur degradation and image restoration, respectively. Differing from the pre-trained deep-learning methods, the proposed DDSR is implemented in a plug-and-play manner, and is capable of restoring HR image from unfavorable LR input with degradations such as partial coverage, noise addition, and darkening. Extensive simulations illustrate the superior performance of the proposed DDSR approach compared to the state-of-the-arts on public datasets with comparable memory load and time consumption, yet exhibiting better application flexibility and convenience, and significantly better generalization ability towards multiple degradations. Our code is available at https://github.com/XYLGroup/DDSR.

NADPHnet: a novel strategy to predict compounds for regulation of NADPH metabolism via network-based methods.

Identification of compounds to modulate NADPH metabolism is crucial for understanding complex diseases and developing effective therapies. However, the complex nature of NADPH metabolism poses challenges in achieving this goal. In this study, we proposed a novel strategy named NADPHnet to predict key proteins and drug-target interactions related to NADPH metabolism via network-based methods. Different from traditional approaches only focusing on one single protein, NADPHnet could screen compounds to modulate NADPH metabolism from a comprehensive view. Specifically, NADPHnet identified key proteins involved in regulation of NADPH metabolism using network-based methods, and characterized the impact of natural products on NADPH metabolism using a combined score, NADPH-Score. NADPHnet demonstrated a broader applicability domain and improved accuracy in the external validation set. This approach was further employed along with molecular docking to identify 27 compounds from a natural product library, 6 of which exhibited concentration-dependent changes of cellular NADPH level within 100 µM, with Oxyberberine showing promising effects even at 10 µM. Mechanistic and pathological analyses of Oxyberberine suggest potential novel mechanisms to affect diabetes and cancer. Overall, NADPHnet offers a promising method for prediction of NADPH metabolism modulation and advances drug discovery for complex diseases.

The effectiveness of intervention measures on MERS-CoV transmission by using the contact networks reconstructed from link prediction data.

Introduction: Mitigating the spread of infectious diseases is of paramount concern for societal safety, necessitating the development of effective intervention measures. Epidemic simulation is widely used to evaluate the efficacy of such measures, but realistic simulation environments are crucial for meaningful insights. Despite the common use of contact-tracing data to construct realistic networks, they have inherent limitations. This study explores reconstructing simulation networks using link prediction methods as an alternative approach. Methods: The primary objective of this study is to assess the effectiveness of intervention measures on the reconstructed network, focusing on the 2015 MERS-CoV outbreak in South Korea. Contact-tracing data were acquired, and simulation networks were reconstructed using the graph autoencoder (GAE)-based link prediction method. A scale-free (SF) network was employed for comparison purposes. Epidemic simulations were conducted to evaluate three intervention strategies: Mass Quarantine (MQ), Isolation, and Isolation combined with Acquaintance Quarantine (AQ + Isolation). Results: Simulation results showed that AQ + Isolation was the most effective intervention on the GAE network, resulting in consistent epidemic curves due to high clustering coefficients. Conversely, MQ and AQ + Isolation were highly effective on the SF network, attributed to its low clustering coefficient and intervention sensitivity. Isolation alone exhibited reduced effectiveness. These findings emphasize the significant impact of network structure on intervention outcomes and suggest a potential overestimation of effectiveness in SF networks. Additionally, they highlight the complementary use of link prediction methods. Discussion: This innovative methodology provides inspiration for enhancing simulation environments in future endeavors. It also offers valuable insights for informing public health decision-making processes, emphasizing the importance of realistic simulation environments and the potential of link prediction methods.

Comparative assessment of deep belief network and hybrid adaptive neuro-fuzzy inference system model based on a meta-heuristic optimization algorithm for precise predictions of the potential evapotranspiration.

Accurately predicting potential evapotranspiration (PET) is crucial in water resource management, agricultural planning, and climate change studies. This research aims to investigate the performance of two machine learning methods, the adaptive network-based fuzzy inference system (ANFIS) and the deep belief network (DBN), in forecasting PET, as well as to explore the potential of hybridizing the ANFIS approach with the Snake Optimizer (ANFIS-SO) algorithm. The study utilized a comprehensive dataset spanning the period from 1983 to 2020. The ANFIS, ANFIS-SO, and DBN models were developed, and their performances were evaluated using statistical metrics, including R2, R adj 2 , NSE, WI, STD, and RMSE. The results showcase the exceptional performance of the DBN model, which achieved R2 and R adj 2 values of 0.99 and NSE and WI scores of 0.99 across the nine stations analyzed. In contrast, the standard ANFIS method exhibited relatively weaker performance, with R2 and R adj 2 values ranging from 0.52 to 0.88. However, the ANFIS-SO approach demonstrated a substantial improvement, with R2 and R adj 2 values ranging from 0.94 to 0.99, suggesting the value of optimization techniques in enhancing the model's capabilities. The Taylor diagram and violin plots with box plots further corroborated the comparative analysis, highlighting the DBN model's superior ability to closely match the observed standard deviation and correlation and its consistent and low-variance predictions. The ANFIS-SO method also exhibited enhanced performance in these visual representations, with an RMSE of 0.86, compared to 0.95 for the standard ANFIS. The insights gained from this study can inform the selection of the most appropriate modeling technique, whether it be the high-precision DBN, the flexible ANFIS, or the optimized ANFIS-SO approach, based on the specific requirements and constraints of the application.

Concordance of HIV Prevention Advocacy Reports and its Associations with HIV Protective Behaviors.

Peer advocacy can promote HIV protective behaviors, but little is known about the concordance on prevention advocacy(PA) reports between people living with HIV(PLWH) and their social network members. We examined prevalence and correlates of such concordance, and its association with the targeted HIV protective behavior of the social network member. Data were analyzed from 193 PLWH(index participants) and their 599 social network members(alters). Kappa statistics measured concordance between index and alter reports of PA in the past 3 months. Logistic and multinomial regressions evaluated the relationship between advocacy concordance and alter condom use and HIV testing behavior and correlates of PA concordance. Advocacy concordance was observed in 0.3% of index-alter dyads for PrEP discussion, 9% for condom use, 18% for HIV testing, 26% for care engagement, and 49% for antiretroviral use discussions. Fewer indexes reported condom use(23.5% vs. 28.1%;[Formula: see text]=3.7, p=0.05) and HIV testing(30.5% vs. 50.5%; [Formula: see text]=25.3, p<0.001) PA occurring. Condom advocacy concordance was higher if the index and alter were romantic partners(OR=3.50; p=0.02), and lower if the index was 10 years younger than the alter(OR=0.23; p = 0.02). Alters had higher odds of using condoms with their main partner when both reported condom advocacy compared to dyads where neither reported advocacy(OR=3.90; p<0.001) and compared to dyads where only the index reported such advocacy(OR = 3.71; p=0.01). Age difference and relationship status impact advocacy agreement, and concordant perceptions of advocacy are linked to increased HIV protective behaviors. Alters' perceptions may be crucial for behavior change, informing strategies for improving advocacy.

Approaches of pre-clinical and clinical trials of repurposed drug.

Medications that are currently on the market and have proven therapeutic usage can have new therapeutic indications discovered through a process called drug repurposing, which is also called drug repositioning. This approach presents a viable method for drug developers and pharmaceutical companies to discern novel targets for FDA-approved medications. Drug repurposing presents several advantages, including reduced time consumption, lower costs, and diminished risk of failure. Sildenafil, commonly known as Viagra, serves as a notable illustration of a repurposed pharmaceutical agent, initially developed and introduced to the market as an antianginal medication. However, in the current context, its application has been redirected towards serving as a pharmaceutical intervention for the treatment of erectile dysfunction. Comparably, a multitude of pharmaceutical agents exist that have demonstrated efficacy in repurposing for therapeutic management of various clinical conditions. Focusing on the historical use of repurposed pharmaceuticals and their present state of application in disease therapies, this chapter seeks to offer a thorough review of drug repurposing methodologies. Furthermore, the rules and regulations that control the repurposing of drugs will be covered in detail in this chapter.

Mixed anxiety-depressive disorder in Parkinson's disease associated with worse resting state functional response to deep brain stimulation of subthalamic nucleus.

Background: Parkinson's disease (PD), even though generally perceived as a dominantly motor disorder, is associated with a wide range of non-motor symptoms, including mixed anxiety-depressive disorder (MADD). Objectives: The aim of the presented study was to determine whether deep brain stimulation (DBS) of the subthalamic nucleus (STN) brings the functional characteristics of non-motor networks closer to the condition detected in healthy population and whether pre-DBS presence of MADD in PD patients was associated with different reaction to this therapeutic modality. Methods: Resting-state fMRI signature elicited by STN DBS activation and deactivation in 81 PD patients was compared against healthy controls, with the focus on measures of efficiency of information processing and localised subnetwork differences. Results: While all the MRI metrics showed statistically significant differences between PD patients in DBS OFF condition and healthy controls, none were detected in such a comparison against DBS ON condition. Furthermore, in the post-DBS evaluation, PD patients with MADD in the pre-DBS stage showed no differences in depression scales compared to pre-DBS psychiatrically intact PD patients, but still exhibited lower DBS-related connectivity in a subnetwork encompassing anterior and posterior cingulate, dorsolateral prefrontal and medial temporal cortices. Conclusions: STN DBS improved all the metrics of interest towards the healthy state, normalising the resting-state MRI signature of PD. Furthermore, pre-DBS presence of MADD, even though clinically silent at post-DBS MRI acquisition, was associated with lower DBS effect in areas highly relevant for depression. This finding points to a possibly latent nature of post-DBS MADD, calling for caution in further follow-up of these patients.

Virtual Screening of Molecules via Neural Fingerprint-based Deep Learning Technique.

A machine learning-based drug screening technique has been developed and optimized using convolutional neural network-derived fingerprints. The optimization of weights in the neural network-based fingerprinting technique was compared with fixed Morgan fingerprints in regard to binary classification on drug-target binding affinity. The assessment was carried out using six different target proteins using randomly chosen small molecules from the ZINC15 database for training. This new architecture proved to be more efficient in screening molecules that less favorably bind to specific targets and retaining molecules that favorably bind to it. Scientific contribution: We have developed a new neural fingerprint-based screening model that has a significant ability to capture hits. Despite using a smaller dataset, this model is capable of mapping chemical space similar to other contemporary algorithms designed for molecular screening. The novelty of the present algorithm lies in the speed with which the models are trained and tuned before testing its predictive capabilities and hence is a significant step forward in the field of machine learning-embedded computational drug discovery.

Disrupted Functional Brain Network Architecture in Sufferers with Boxing-Related Repeated Mild Traumatic Brain Injury: A Resting-State EEG Study.

BACKGROUND: Repetitive mild traumatic brain injury (rmTBI) often occurs in individuals engaged in contact sports, particularly boxing. This study aimed to elucidate the effects of rmTBI on phase-locking value (PLV)-based graph theory and functional network architecture in individuals with boxing-related injuries in five frequency bands by employing resting-state electroencephalography (EEG). METHODS: Twenty-fore professional boxers and 25 matched healthy controls were recruited to perform a resting-state task, and their noninvasive scalp EEG data were collected simultaneously. Based on the construction of PLV matrices for boxers and controls, phase synchronization and graph-theoretic characteristics were identified in each frequency band. The significance of the calculated functional brain networks between the two populations was analyzed using a network-based statistical (NBS) approach. RESULTS: Compared to controls, boxers exhibited an increasing trend in PLV synchronization and notable differences in the distribution of functional centers, especially in the gamma frequency band. Additionally, attenuated nodal network parameters and decreased small-world measures were observed in the theta, beta, and gamma bands, suggesting that the functional network efficiency and small-world characteristics were significantly weakened in boxers. NBS analysis revealed that boxers exhibited a significant increase in network connectivity strength compared to controls in the theta, beta, and gamma frequency bands. The functional connectivity of the significance subnetworks exhibited an asymmetric distribution between the bilateral hemispheres, indicating that the optimized organization of information integration and segregation for the resting-state networks was imbalanced and disarranged for boxers. CONCLUSIONS: This is the first study to investigate the underlying deficits in PLV-based graph-theoretic characteristics and NBS-based functional networks in patients with rmTBI from the perspective of whole-brain resting-state EEG. Joint analyses of distinctive graph-theoretic representations and asymmetrically hyperconnected subnetworks in specific frequency bands may serve as an effective method to assess the underlying deficiencies in resting-state network processing in patients with sports-related rmTBI.

Correlation between autistic traits and brain functional connectivity in preschoolers with autism spectrum disorder: a resting state MEG study.

BACKGROUND: Neurophysiological studies recognized that Autism Spectrum Disorder (ASD) is associated with altered patterns of over- and under-connectivity. However, little is known about network organization in children with ASD in the early phases of development and its correlation with the severity of core autistic features. METHODS: The present study aimed at investigating the association between brain connectivity derived from MEG signals and severity of ASD traits measured with different diagnostic clinical scales, in a sample of 16 children with ASD aged 2 to 6 years. RESULTS: A significant correlation emerged between connectivity strength in cortical brain areas implicated in several resting state networks (Default mode, Central executive, Salience, Visual and Sensorimotor) and the severity of communication anomalies, social interaction problems, social affect problems, and repetitive behaviors. Seed analysis revealed that this pattern of correlation was mainly caused by global rather than local effects. CONCLUSIONS: The present evidence suggests that altered connectivity strength in several resting state networks is related to clinical features and may contribute to neurofunctional correlates of ASD. Future studies implementing the same method on a wider and stratified sample may further support functional connectivity as a possible biomarker of the condition.