Abnormal brain structure as a potential biomarker for venous erectile dysfunction: evidence from multimodal MRI and machine learning

Revolutionizing core muscle analysis in female sexual dysfunction based on machine learning

The purpose of this study is to investigate the role of core muscles in female sexual dysfunction (FSD) and develop comprehensive rehabilitation programs to address this issue. We aim to answer the following research questions: what are the roles of core muscles in FSD, and how can machine and deep learning models accurately predict changes in core muscles during FSD? FSD is a common condition that affects women of all ages, characterized by symptoms such as decreased libido, difficulty achieving orgasm, and pain during intercourse. We conducted a comprehensive analysis of changes in core muscles during FSD using machine and deep learning. We evaluated the performance of multiple models, including multi-layer perceptron (MLP), long short-term memory (LSTM), convolutional neural network (CNN), recurrent neural network (RNN), ElasticNetCV, random forest regressor, SVR, and Bagging regressor. The models were evaluated based on mean squared error (MSE), mean absolute error (MAE), and R-squared (R2) score. Our results show that CNN and random forest regressor are the most accurate models for predicting changes in core muscles during FSD. CNN achieved the lowest MSE (0.002) and the highest R2 score (0.988), while random forest regressor also performed well with an MSE of 0.0021 and an R2 score of 0.9905. Our study demonstrates that machine and deep learning models can accurately predict changes in core muscles during FSD. The neglected core muscles play a significant role in FSD, highlighting the need for comprehensive rehabilitation programs that address these muscles. By developing these programs, we can improve the quality of life for women with FSD and help them achieve optimal sexual health.

Artificial Intelligence in Andrology: From Semen Analysis to Image Diagnostics

Artificial intelligence (AI) in medicine has gained a lot of momentum in the last decades and has been applied to various fields of medicine. Advances in computer science, medical informatics, robotics, and the need for personalized medicine have facilitated the role of AI in modern healthcare. Similarly, as in other fields, AI applications, such as machine learning, artificial neural networks, and deep learning, have shown great potential in andrology and reproductive medicine. AI-based tools are poised to become valuable assets with abilities to support and aid in diagnosing and treating male infertility, and in improving the accuracy of patient care. These automated, AI-based predictions may offer consistency and efficiency in terms of time and cost in infertility research and clinical management. In andrology and reproductive medicine, AI has been used for objective sperm, oocyte, and embryo selection, prediction of surgical outcomes, cost-effective assessment, development of robotic surgery, and clinical decision-making systems. In the future, better integration and implementation of AI into medicine will undoubtedly lead to pioneering evidence-based breakthroughs and the reshaping of andrology and reproductive medicine.

Cardiovascular/Stroke Risk Assessment in Patients with Erectile Dysfunction-A Role of Carotid Wall Arterial Imaging and Plaque Tissue Characterization Using Artificial Intelligence Paradigm: A Narrative Review

PURPOSE

The role of erectile dysfunction (ED) has recently shown an association with the risk of stroke and coronary heart disease (CHD) via the atherosclerotic pathway. Cardiovascular disease (CVD)/stroke risk has been widely understood with the help of carotid artery disease (CTAD), a surrogate biomarker for CHD. The proposed study emphasizes artificial intelligence-based frameworks such as machine learning (ML) and deep learning (DL) that can accurately predict the severity of CVD/stroke risk using carotid wall arterial imaging in ED patients.

METHODS

Using the PRISMA model, 231 of the best studies were selected. The proposed study mainly consists of two components: (i) the pathophysiology of ED and its link with coronary artery disease (COAD) and CHD in the ED framework and (ii) the ultrasonic-image morphological changes in the carotid arterial walls by quantifying the wall parameters and the characterization of the wall tissue by adapting the ML/DL-based methods, both for the prediction of the severity of CVD risk. The proposed study analyzes the hypothesis that ML/DL can lead to an accurate and early diagnosis of the CVD/stroke risk in ED patients. Our finding suggests that the routine ED patient practice can be amended for ML/DL-based CVD/stroke risk assessment using carotid wall arterial imaging leading to fast, reliable, and accurate CVD/stroke risk stratification.

SUMMARY

We conclude that ML and DL methods are very powerful tools for the characterization of CVD/stroke in patients with varying ED conditions. We anticipate a rapid growth of these tools for early and better CVD/stroke risk management in ED patients.

Convergent and Divergent Structural Connectivity of Brain White Matter Network Between Patients With Erectile Dysfunction and Premature Ejaculation: A Graph Theory Analysis Study

Background

Sexual dysfunction, namely, erectile dysfunction (ED) and premature ejaculation (PE), has been found to be associated with abnormal structural connectivity in the brain. Previous studies have mainly focused on a single disorder, however, convergent and divergent structural connectivity patterns of the brain network between ED and PE remain poorly understood.

Methods

T1-weighted structural data and diffusion tensor imaging data of 28 patients with psychological ED, 28 patients with lifelong PE (LPE), and 28 healthy controls (HCs) were obtained to map the white matter (WM) brain networks. Then, the graph-theoretical method was applied to investigate the differences of network properties (small-world measures) of the WM network between patients with ED and LPE. Furthermore, nodal segregative and integrative parameters (nodal clustering coefficient and characteristic path length) were also explored between these patients.

Results

Small-world architecture of the brain networks were identified for both psychological ED and LPE groups. However, patients with ED exhibited increased average characteristic path length of the brain network when compared with patients with LPE and HCs. No significant difference was found in the average characteristic path length between patients with LPE and HCs. Moreover, increased nodal characteristic path length was found in the right middle frontal gyrus (orbital part) of patients with ED and LPE when compared with HCs. In addition, patients with ED had increased nodal characteristic path length in the right middle frontal gyrus (orbital part) when compared with patients with LPE.

Conclusion

Together, our results demonstrated that decreased integration of the right middle frontal gyrus (orbital part) might be a convergent neuropathological basis for both psychological ED and LPE. In addition, patients with ED also exhibited decreased integration in the whole WM brain network, which was not found in patients with LPE. Therefore, altered integration of the whole brain network might be the divergent structural connectivity patterns for psychological ED and LPE.

Diagnostic work up: Laboratory and biomarkers

This chapter will focus on the diagnostic work around sexual dysfunction in Parkinson's disease, especially laboratory tests and biomarkers. A number of methods to analyze if sexual dysfunction is caused by neural pathology, vascular dysfunction or other mechanisms are now available. Other methods can be used to differentiate between psychogenic/functional reasons behind sexual dysfunction and organic ones. The role of biomarkers for diagnosis, but also for understanding the reason behind and for counteracting sexual dysfunction is becoming more evident. There is also a rich and increasing number of scales and other instruments available for detecting and quantifying sexual hypo- and hyperactivity. When investigating the reason behind sexual dysfunction in patients with Parkinson's disease comorbidities should also be considered. Finally, early and pronounced sexual dysfunction might in some cases be an indication that differential diagnosis, like Multisystem Atrophy, should be thought about. All these aspects of the diagnostic procedures around sexual dysfunction in Parkinson's disease will be covered in this chapter.

Applications of artificial intelligence in the diagnosis and prediction of erectile dysfunction: a narrative review

Despite the high prevalence of erectile dysfunction, patients are reluctant to seek medical advice, which leads to low diagnostic rates in clinical practice. Artificial intelligence has been widely applied in the diagnosis of many diseases and may alleviate the situation. However, the applications of artificial intelligence in erectile dysfunction have not been reviewed to date. Therefore, the assistance from artificial intelligence needs to be summarized. In this review, 418 publications before January 10, 2021, regarding artificial intelligence applications in diagnosing and predicting erectile dysfunction, were retrieved from five databases, including PubMed, EMBASE, the Cochrane Library, and two Chinese databases (WANFANG and CNKI). In addition, the reference lists of the included studies or relevant reviews were checked to avoid bias. Finally, 30 articles were reviewed to summarize the current status, merits, and limitations of applying artificial intelligence in diagnosing and predicting erectile dysfunction. The results showed that artificial intelligence contributed to developing novel diagnostic questionnaires, equipment, expert systems, classifiers by images and predictive models. However, most of the included studies were not subjected to external validations, resulting in doubt on the generalizability. In the future, more rigorously designed studies with high-quality datasets for erectile dysfunction are required.

Decreased gray matter volume of the anterior insular cortex in patients with psychogenic erectile dysfunction: A voxel-based morphometry study

This study aimed to explore the gray matter morphological alteration and its correlation with the severity of symptoms in patients with psychogenic erectile dysfunction (pED). Fifty patients with pED and 50 healthy controls (HCs) were included. The whole-brain voxel-based morphometry analysis was conducted to compare the differences in gray matter volume (GMV) between patients with pED and HCs. And then, the region-of-interest-based correlation analyses were performed between the GMV of these regions with the most pronounced between-group differences and clinical symptoms in patients. The results demonstrated that patients with pED manifested decreased GMV in the bilateral anterior insula (aINS), bilateral precentral gyrus, left postcentral gyrus, bilateral anterior cingulate cortex, bilateral middle cingulate cortex, bilateral fusiform gyrus, and cerebellum when compared to HCs. Taking the bilateral aINS as the region-of-interest, the results of voxel-based correlation analyses showed that the GMV of the bilateral aINS were positively correlated with the International Index of Erectile Function 5 (IIEF-5) and Quality of Erection Questionnaire score, and the GMV of right aINS was positively associated with duration and sexual craving score in patients with pED. Furthermore, the significant correlations between the total GMV of the right aINS and IIEF-5 and sexual craving score, as well as between the total GMV of the left aINS and sexual craving score were also detected. In conclusion, these results suggested that the decreased GMV of aINS might be a critical neuropathological characteristic of pED, which provided new evidence for understanding the neurobiological basis of pED from the perspective of brain structure alterations.

Abnormal Functional Connectivity Between the Left Medial Superior Frontal Gyrus and Amygdala Underlying Abnormal Emotion and Premature Ejaculation: A Resting State fMRI Study

Introduction

Premature ejaculation (PE) is a common sexual dysfunction and is found to be associated with abnormal emotion. The amygdala plays an important role in the processing of emotion. The process of ejaculation is found to be mediated by the frontal-limbic neural circuits. However, the correlations between PE and emotion are still unclear.

Methods

Resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired in 27 PE patients with stable emotion (SPE), 27 PE patients with abnormal emotion (NPE), and 30 healthy controls (HC). We used rs-fMRI to explore the underlying neural mechanisms in SPE, NPE, and HC by measuring the functional connectivity (FC). Differences of FC values among the three groups were compared when choosing bilateral amygdala as the regions of interest (ROIs). We also explored the correlations between the brain regions showing altered FC values and scores of the premature ejaculation diagnostic tool (PEDT)/Eysenck Personality Inventory about neuroticism (EPQ-N) in the PE group.

Results

When the left amygdala was chosen as the ROI, the SPE group exhibited an increased FC between the left medial superior frontal gyrus (SFGmed) and amygdala compared with the NPE or HC group. When the right amygdala was chosen as the ROI, the NPE group exhibited a decreased FC between the left SFGmed and right amygdala compared with the HC group. In addition, FC values of the left SFGmed had positive correlations with PEDT and negative correlations with EPQ-N scores in the PE group. Moreover, FC values of the left superior temporal gyrus had positive correlations with EPQ-N scores in the PE group.

Conclusion

The increased FC values between the left SFGmed and amygdala could reflect a compensatory cortical control mechanism with the effect of stabilized emotion in the limbic regions of PE patients. Abnormal FC between these brain regions could play a critical role in the physiopathology of PE and could help us in dividing PE into more subtypes.

Characteristics of mental health implications and plasma metabolomics in patients recently recovered from COVID-19

This study aimed to explore the associations between cerebral white matter (WM) alterations, mental health status, and metabolism in recovered COVID-19 patients. We included 28 recovered COVID-19 patients and 27 healthy controls between April 2020 and June 2020. Demographic data, the mental health scores, diffusion-tensor imaging (DTI) data, and plasma metabolomics were collected and compared between the two groups. Tract-based spatial statistics and graph theory approaches were used for DTI data analysis. Untargeted metabolomics analysis of the plasma was performed. Correlation analyses were performed between these characteristics. Recovered COVID-19 patients showed decreased fractional anisotropy, increased mean diffusivity and radial diffusivity values in widespread brain regions, and significantly lower global efficiency, longer shortest path length, and less nodal local efficiency in superior occipital gyrus (all, P < 0.05, Bonferroni corrected). Our results also demonstrated significantly different plasma metabolic profiling in recovered COVID-19 patients even at 3 months after their hospital discharge, which was mainly related to purine pathways, amino acids, lipids, and amine metabolism. Certain regions with cerebral WM alterations in the recovered patients showed significant correlations with different metabolites and the mental health scores. We observed multiple alterations in both WM integrity and plasma metabolomics that may explain the deteriorated mental health of recovered COVID-19 patients. These findings may provide potential biomarkers for the mental health evaluation for the recovered COVID-19 patients and potential targets for novel therapeutics.

Risk factors related to the severity of COVID-19 in Wuhan

Objective: To evaluate the characteristics at admission of patients with moderate COVID-19 in Wuhan and to explore risk factors associated with the severe prognosis of the disease for prognostic prediction. Methods: In this retrospective study, moderate and severe disease was defined according to the report of the WHO-China Joint Mission on COVID-19. Clinical characteristics and laboratory findings of 172 patients with laboratory-confirmed moderate COVID-19 were collected when they were admitted to the Cancer Center of Wuhan Union Hospital between February 13, 2020 and February 25, 2020. This cohort was followed to March 14, 2020. The outcomes, being discharged as mild cases or developing into severe cases, were categorized into two groups. The data were compared and analyzed with univariate logistic regression to identify the features that differed significantly between the two groups. Based on machine learning algorithms, a further feature selection procedure was performed to identify the features that can contribute the most to the prediction of disease severity. Results: Of the 172 patients, 112 were discharged as mild cases, and 60 developed into severe cases. Four clinical characteristics and 18 laboratory findings showed significant differences between the two groups in the statistical test (P<0.01) and univariate logistic regression analysis (P<0.01). In the further feature selection procedure, six features were chosen to obtain the best performance in discriminating the two groups with a linear kernel support vector machine. The mean accuracy was 91.38%, with a sensitivity of 0.90 and a specificity of 0.94. The six features included interleukin-6, high-sensitivity cardiac troponin I, procalcitonin, high-sensitivity C-reactive protein, chest distress and calcium level. Conclusions: With the data collected at admission, the combination of one clinical characteristic and five laboratory findings contributed the most to the discrimination between the two groups with a linear kernel support vector machine classifier. These factors may be risk factors that can be used to perform a prognostic prediction regarding the severity of the disease for patients with moderate COVID-19 in the early stage of the disease.

Radiomics Signature: A potential biomarker for the prediction of survival in Advanced Hepatocellular Carcinoma

Objectives: To develop and validate radiomics nomograms for the pretreatment predictions of overall survival (OS) and time to progression (TTP) in the patients with advanced hepatocellular carcinoma (HCC) treated with apatinib plus transarterial chemoembolization (TACE), and to assess the incremental value of the clinical-radiomics nomograms for estimating individual OS and TTP. Methods: A total of 60 patients with advanced HCC (BCLC stage C) treated with apatinib plus TACE were divided into a training set (n=48) and a validation set (n=12). The predictors identified from the clinical variables and the radiomics signature constructed from the computed tomography images, such as ɑ-fetoprotein level (AFP), formfactor, the grey level co-occurrence matrix, the gray level size zone matrix, and the gray level run-length matrix, were used to build the clinical-radiomics nomograms and the radiomics nomograms for the prediction of OS and TTP. Results: Apatinib plus TACE benefited the patients with advanced HCC, with a 579-day median OS and a 270-day median TTP. The nomograms were built with the radiomics signature and AFP, and achieved favorable prediction efficacy with acceptable calibration curves. Decision curve analyses demonstrated that the clinical-radiomics nomograms outperformed the radiomics nomograms for the predictions of OS and TTP. Conclusions: Apatinib plus TACE may improve OS and prolonged TTP in the patients with advanced HCC. The clinical-radiomics nomograms, a noninvasive pretreatment prediction tool that incorporate radiomics signature and AFP, demonstrated good prediction accuracy for OS and TTP in these patients. These results indicate that the clinical-radiomics nomograms may provide novel insight for precise personalized medicine approaches in the patients with advanced HCC.

Effects of Chaihu-Shugan-San capsule for psychogenic erectile dysfunction: Study protocol of a randomized placebo-controlled trial

BACKGROUND

Erectile dysfunction (ED) affects many adult men worldwide. Many studies on the brain of psychogenic ED have shown significant cerebral functional changes and reduced volume of gray matter and white matter microstructural alterations in widespread brain regions. Chaihu-Shugan-San (CHSGS) capsule has been used to treat ED from the 20th century in China. However, clinical research of CHSGS capsule in the treatment of ED was lack. We design this study to evaluate the efficacy and safety of CHSGS capsule in the treatment of patients suffering from psychogenic ED. Furthermore, we also aim to provide a new evidence as well as an innovation of the clinical treatment in psychogenic ED.

METHODS

This study is designed as a multi-center, 3-arms, randomized trial. From the perspective of psychogenic ED, we will divide patients into 3 groups, which are placebo group, tadalafil group and CHSGS group. One hundred thirty-five patients will be randomly allocated to receive placebo, CHSGS capsule or tadalafil oral pharmacotherapy. After the period of 4-week treatment, the outcome of primary assessment changes in the brain MRI, IIEF-5, EHS, and QEQ total scores from baseline. Secondary assessments include the SEAR, HAMA-14, HAMD-17 scores, response rate of the patients and their partners.

DISCUSSION

We designed this study based on previous research about psychogenic erectile dysfunction (ED). This study will provide objective evidences to evaluate the effects of CHSGS capsule as an adjuvant treatment for psychogenic ED.

TRIAL REGISTRATION NUMBER

chictr.org.cn, ChiCTR-IOR-1800018301.

Abnormal Resting-State Functional Connectivity in the Whole Brain in Lifelong Premature Ejaculation Patients Based on Machine Learning Approach

Recent neuroimaging studies have indicated that abnormalities in brain structure and function may play an important role in the etiology of lifelong premature ejaculation (LPE). LPE patients have exhibited aberrant cortical structure, altered brain network function and abnormal brain activation in response to erotic pictures. However, it remains unclear whether resting-state whole brain functional connectivity (FC) is altered in LPE patients. Machine learning analysis has the advantage of screening the best classification features from high-throughput data (such as FC), which has the potential to identify the pathophysiological targets of disease by establishing classification indicators for patients and healthy controls (HCs). Therefore, the supported vector machine based classification model using FC as features was used in the present study to confirm the most specific FCs that distinguish LPE patients from healthy controls. After feature selection, the remained features were used to build the classification model, with an accuracy 0.85 ± 0.14, sensitivity of 0.92 ± 0.18, specificity of 0.72 ± 0.30, and recall index of 0.85 ± 0.17 across 1000 testing groups (100 times 10-folds cross validation). After that, two-sample t-tests with family-wise error correction were used to compare these features that occur more than 500 times during training steps between LPE patients and HCs. Four FCs, (1) between left medial part of orbital frontal cortex (mOFC) and right mOFC, (2) between the left rectus and right postcentral gyrus, (3) between the right insula and left pallidum, and (4) between the right middle part of temporal pole and right inferior part of temporal gyrus showed significant group difference. These results demonstrate that resting-state brain FC might be a discriminating feature to distinguish LPE patients from HCs. These classification features, especially the FC between bilateral mOFC, provide underlying abnormal central functional targets in LPE etiology, which offers a novel alternative target for future intervention in LPE treatment.

The autism/neuroprotection-linked ADNP/NAP regulate the excitatory glutamatergic synapse

Activity-dependent neuroprotective protein (ADNP), essential for brain formation, was discovered as a leading de novo mutated gene causing the autism-like ADNP syndrome. This syndrome is phenotypically characterized by global developmental delays, intellectual disabilities, speech impediments, and motor dysfunctions. The Adnp haploinsufficient mouse mimics the human ADNP syndrome in terms of synapse density and gene expression patterns, as well as in developmental, motor, and cognitive abilities. Peripheral ADNP was also discovered as a biomarker for Alzheimer's disease and schizophrenia, with nasal administration of the ADNP snippet peptide NAP (enhancing endogenous ADNP activity) leading to partial cognitive and functional protection at the cellular, animal and clinical settings. Here, a novel formulation for effective delivery of NAP is provided with superior brain penetration capabilities. Also provided are methods for treating pertinent clinical implications such as autism, cognitive impairments, olfactory deficits, and muscle strength using the formulation in the Adnp haploinsufficient mouse. Results showed a dramatically specific increase in brain/body bioavailability with the new formulation, without breaching the blood brain barrier. Additional findings included improvements using daily intranasal treatments with NAP, at the behavioral and brain structural levels, diffusion tensor imaging (DTI), translatable to clinical practice. Significant effects on hippocampal and cerebral cortical expression of the presynaptic Slc17a7 gene encoding vesicular excitatory glutamate transporter 1 (VGLUT1) were observed at the RNA and immunohistochemical levels, explaining the DTI results. These findings tie for the first time a reduction in presynaptic glutamatergic synapses with the autism/Alzheimer's/schizophrenia-linked ADNP deficiency coupled with amelioration by NAP (CP201).

Value of flaccid penile ultrasound in screening for arteriogenic impotence: a preliminary prospective study

Background

This prospective study is to evaluate the potential value of sonographic measurements in the flaccid penis for the screening of arteriogenic impotence.

Methods

A consecutive series of 260 Chinese males consulting for sexual dysfunction and 54 controls underwent sonographic examination. The sonographic parameters were correlated with the clinical gold standards, including the international index of erectile function (IIEF) and penile erectile hardness grading scale (EHGS). The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and area under the receiver operating characteristic curve (AUROC) of flaccid peak systolic velocity (PSV) in predicting patients with normal function were analyzed.

Results

The mean cavernous PSV of both sides in the patients with sexual dysfunction ranged from 7.76 to 11.12 cm/sec with a stepwise increase in IIEF and EHGS grading scale (P < .05). The cutoff value of flaccid PSV for the differential diagnosis of grade 4 of IIEF-5 or EHGS was 8.20–8.90 cm/sec, with an AUROC of 0.657–0.724, specificity of 82.96–86.84% and PPV of 95.20–96.60%, respectively.

Conclusions

This simple flaccid PSV measurement is a specific tool for screening arteriogenic impotence.