A review of the clinical efficacy of FDA-approved antibody‒drug conjugates in human cancers.

While strategies such as chemotherapy and immunotherapy have become the first-line standard therapies for patients with advanced or metastatic cancer, acquired resistance is still inevitable in most cases. The introduction of antibody‒drug conjugates (ADCs) provides a novel alternative. ADCs are a new class of anticancer drugs comprising the coupling of antitumor mAbs with cytotoxic drugs. Compared with chemotherapeutic drugs, ADCs have the advantages of good tolerance, accurate target recognition, and small effects on noncancerous cells. ADCs occupy an increasingly important position in the therapeutic field. Currently, there are 13 Food and Drug Administration (FDA)‒approved ADCs and more than 100 ADC drugs at different stages of clinical trials. This review briefly describes the efficacy and safety of FDA-approved ADCs, and discusses the related problems and challenges to provide a reference for clinical work.

Development and validation of [18 F]-PSMA-1007 PET-based radiomics model to predict biochemical recurrence-free survival following radical prostatectomy.

PURPOSE: Biochemical recurrence (BCR) following radical prostatectomy (RP) is a significant concern for patients with prostate cancer. Reliable prediction models are needed to identify patients at risk for BCR and facilitate appropriate management. This study aimed to develop and validate a clinical-radiomics model based on preoperative [18 F]PSMA-1007 PET for predicting BCR-free survival (BRFS) in patients who underwent RP for prostate cancer. MATERIALS AND METHODS: A total of 236 patients with histologically confirmed prostate cancer who underwent RP were retrospectively analyzed. All patients had a preoperative [18 F]PSMA-1007 PET/CT scan. Radiomics features were extracted from the primary tumor region on PET images. A radiomics signature was developed using the least absolute shrinkage and selection operator (LASSO) Cox regression model. The performance of the radiomics signature in predicting BRFS was assessed using Harrell's concordance index (C-index). The clinical-radiomics nomogram was constructed using the radiomics signature and clinical features. The model was externally validated in an independent cohort of 98 patients. RESULTS: The radiomics signature comprised three features and demonstrated a C-index of 0.76 (95% CI: 0.60-0.91) in the training cohort and 0.71 (95% CI: 0.63-0.79) in the validation cohort. The radiomics signature remained an independent predictor of BRFS in multivariable analysis (HR: 2.48, 95% CI: 1.47-4.17, p < 0.001). The clinical-radiomics nomogram significantly improved the prediction performance (C-index: 0.81, 95% CI: 0.66-0.95, p = 0.007) in the training cohort and (C-index: 0.78 95% CI: 0.63-0.89, p < 0.001) in the validation cohort. CONCLUSION: We developed and validated a novel [18 F]PSMA-1007 PET-based clinical-radiomics model that can predict BRFS following RP in prostate cancer patients. This model may be useful in identifying patients with a higher risk of BCR, thus enabling personalized risk stratification and tailored management strategies.

Exploring Bitter and Sweet: The Application of Large Language Models in Molecular Taste Prediction.

The perception of bitter and sweet tastes is a crucial aspect of human sensory experience. Concerns over the long-term use of aspartame, a widely used sweetener suspected of carcinogenic risks, highlight the importance of developing new taste modifiers. This study utilizes Large Language Models (LLMs) such as GPT-3.5 and GPT-4 for predicting molecular taste characteristics, with a focus on the bitter-sweet dichotomy. Employing random and scaffold data splitting strategies, GPT-4 demonstrated superior performance, achieving an impressive 86% accuracy under scaffold partitioning. Additionally, ChatGPT was employed to extract specific molecular features associated with bitter and sweet tastes. Utilizing these insights, novel molecular compounds with distinct taste profiles were successfully generated. These compounds were validated for their bitter and sweet properties through molecular docking and molecular dynamics simulation, and their practicality was further confirmed by ADMET toxicity testing and DeepSA synthesis feasibility. This research highlights the potential of LLMs in predicting molecular properties and their implications in health and chemical science.

Building a Kokumi Database and Machine Learning-Based Prediction: A Systematic Computational Study on Kokumi Analysis.

Kokumi is a subtle sensation characterized by a sense of fullness, continuity, and thickness. Traditional methods of taste discovery and analysis, including those of kokumi, have been labor-intensive and costly, thus necessitating the emergence of computational methods as critical strategies in molecular taste analysis and prediction. In this study, we undertook a comprehensive analysis, prediction, and screening of the kokumi compounds. We categorized 285 kokumi compounds from a previously unreleased kokumi database into five groups based on their molecular characteristics. Moreover, we predicted kokumi/non-kokumi and multi-flavor compositions using six structure-taste relationship models: MLP-E3FP, MLP-PLIF, MLP-RDKFP, SVM-RDKFP, RF-RDKFP, and WeaveGNN feature of Atoms and Bonds. These six predictors exhibited diverse performance levels across two different models. For kokumi/non-kokumi prediction, the WeaveGNN model showed an exceptional predictive AUC value (0.94), outperforming the other models (0.87, 0.90, 0.89, 0.92, and 0.78). For multi-flavor prediction, the MLP-E3FP model demonstrated a higher predictive AUC and MCC value (0.94 and 0.74) than the others (0.73 and 0.33; 0.92 and 0.70; 0.95 and 0.73; 0.94 and 0.64; and 0.88 and 0.69). This data highlights the model's proficiency in accurately predicting kokumi molecules. As a result, we sourced kokumi active compounds through a high-throughput screening of over 100 million molecules, further refined by toxicity and similarity screening. Lastly, we launched a web platform, KokumiPD (https://www.kokumipd.com/), offering a comprehensive kokumi database and online prediction services for users.

Integrating Computational and Experimental Methods to Identify Novel Sweet Peptides from Egg and Soy Proteins.

Sweetness in food delivers a delightful sensory experience, underscoring the crucial role of sweeteners in the food industry. However, the widespread use of sweeteners has sparked health concerns. This underscores the importance of developing and screening natural, health-conscious sweeteners. Our study represents a groundbreaking venture into the discovery of such sweeteners derived from egg and soy proteins. Employing virtual hydrolysis as a novel technique, our research entailed a comprehensive screening process that evaluated biological activity, solubility, and toxicity of the derived compounds. We harnessed cutting-edge machine learning methodologies, specifically the latest graph neural network models, for predicting the sweetness of molecules. Subsequent refinements were made through molecular docking screenings and molecular dynamics simulations. This meticulous research approach culminated in the identification of three promising sweet peptides: DCY(Asp-Cys-Tyr), GGR(Gly-Gly-Arg), and IGR(Ile-Gly-Arg). Their binding affinity with T1R2/T1R3 was lower than -15 kcal/mol. Using an electronic tongue, we verified the taste profiles of these peptides, with IGR emerging as the most favorable in terms of taste with a sweetness value of 19.29 and bitterness value of 1.71. This study not only reveals the potential of these natural peptides as healthier alternatives to traditional sweeteners in food applications but also demonstrates the successful synergy of computational predictions and experimental validations in the realm of flavor science.

Unveiling Anti-Diabetic Potential of Baicalin and Baicalein from Baikal Skullcap: LC-MS, In Silico, and In Vitro Studies.

Type 2 diabetes mellitus (T2DM) is marked by persistent hyperglycemia, insulin resistance, and pancreatic ß-cell dysfunction, imposing substantial health burdens and elevating the risk of systemic complications and cardiovascular diseases. While the pathogenesis of diabetes remains elusive, a cyclical relationship between insulin resistance and inflammation is acknowledged, wherein inflammation exacerbates insulin resistance, perpetuating a deleterious cycle. Consequently, anti-inflammatory interventions offer a therapeutic avenue for T2DM management. In this study, a herb called Baikal skullcap, renowned for its repertoire of bioactive compounds with anti-inflammatory potential, is posited as a promising source for novel T2DM therapeutic strategies. Our study probed the anti-diabetic properties of compounds from Baikal skullcap via network pharmacology, molecular docking, and cellular assays, concentrating on their dual modulatory effects on diabetes through Protein Tyrosine Phosphatase 1B (PTP1B) enzyme inhibition and anti-inflammatory actions. We identified the major compounds in Baikal skullcap using liquid chromatography-mass spectrometry (LC-MS), highlighting six flavonoids, including the well-studied baicalein, as potent inhibitors of PTP1B. Furthermore, cellular experiments revealed that baicalin and baicalein exhibited enhanced anti-inflammatory responses compared to the active constituents of licorice, a known anti-inflammatory agent in TCM. Our findings confirmed that baicalin and baicalein mitigate diabetes via two distinct pathways: PTP1B inhibition and anti-inflammatory effects. Additionally, we have identified six flavonoid molecules with substantial potential for drug development, thereby augmenting the T2DM pharmacotherapeutic arsenal and promoting the integration of herb-derived treatments into modern pharmacology.

Mulberry Leaf Compounds and Gut Microbiota in Alzheimer's Disease and Diabetes: A Study Using Network Pharmacology, Molecular Dynamics Simulation, and Cellular Assays.

Recently, studies have reported a correlation that individuals with diabetes show an increased risk of developing Alzheimer's disease (AD). Mulberry leaves, serving as both a traditional medicinal herb and a food source, exhibit significant hypoglycemic and antioxidative properties. The flavonoid compounds in mulberry leaf offer therapeutic effects for relieving diabetic symptoms and providing neuroprotection. However, the mechanisms of this effect have not been fully elucidated. This investigation aimed to investigate the combined effects of specific mulberry leaf flavonoids (kaempferol, quercetin, rhamnocitrin, tetramethoxyluteolin, and norartocarpetin) on both type 2 diabetes mellitus (T2DM) and AD. Additionally, the role of the gut microbiota in these two diseases' treatment was studied. Using network pharmacology, we investigated the potential mechanisms of flavonoids in mulberry leaves, combined with gut microbiota, in combating AD and T2DM. In addition, we identified protein tyrosine phosphatase 1B (PTP1B) as a key target for kaempferol in these two diseases. Molecular docking and molecular dynamics simulations showed that kaempferol has the potential to inhibit PTP1B for indirect treatment of AD, which was proven by measuring the IC50 of kaempferol (279.23 µM). The cell experiment also confirmed the dose-dependent effect of kaempferol on the phosphorylation of total cellular protein in HepG2 cells. This research supports the concept of food-medicine homology and broadens the range of medical treatments for diabetes and AD, highlighting the prospect of integrating traditional herbal remedies with modern medical research.

PSMC4 promotes prostate carcinoma progression by regulating the CBX3-EGFR-PI3K-AKT-mTOR pathway.

Proteasome 26S subunit ATPase 4 (PSMC4) could regulate cancer progression. However, the function of PSMC4 in prostate carcinoma (PCa) progression requires further clarification. In the study, PSMC4 and chromobox 3 (CBX3) levels were verified by TCGA data and tissue microarrays. Cell counting kit-8, cell apoptosis, cell cycle, wound healing, transwell and xenograft tumour model assays were performed to verify biological functions of PSMC4 in PCa. RNA-seq, PCR, western blotting and co-IP assays were performed to verify the mechanism of PSMC4. Results showed that PSMC4 level was significantly increased in PCa tissues, and patients with PCa with a high PSMC4 level exhibited shorter overall survival. PSMC4 knockdown markedly inhibited cell proliferation, cell cycle and migration in vitro and in vivo, and significantly promoted cell apoptosis. Then further study revealed that CBX3 was a downstream target of PSMC4. PSMC4 knockdown markedly reduced CBX3 level, and inhibited PI3K-AKT-mTOR signalling. CBX3 overexpression markedly promoted epidermal growth factor receptor (EGFR) level. Finally, PSMC4 overexpression showed reverse effect in DU145 cells, and the effects of PSMC4 overexpression on cell proliferation, migration and clonal formation were rescued by the CBX3 knockdown, and regulated EGFR-PI3K-AKT-mTOR signalling. In conclusion, PSMC4 could regulate the PCa progression by mediating the CBX3-EGFR-PI3K-AKT-mTOR pathway. These findings provided a new target for PCa treatment.

Investigation on the mechanisms of human sperm DNA damage based on the proteomics analysis by SWATH-MS.

BACKGROUND: Spermatozoa have the task of delivering an intact paternal genome to the oocyte and supporting successful embryo development. The detection of sperm DNA fragmentation (SDF) has been emerging as a complementary test to conventional semen analysis for male infertility evaluation, but the mechanism leading to SDF and its impact on assisted reproduction remain unclear. Therefore, the study identified and analyzed the differentially expressed proteins of sperm with high and low SDF. METHODS: Semen samples from men attended the infertility clinic during June 2020 and August 2020 were analyzed, and sperm DNA fragmentation index (DFI) was detected by the sperm chromatin structure assay. Semen samples with low DFI (< 30%, control group) and high DFI (≥ 30%, experimental group) were optimized by density gradient centrifugation (DGC), and the differentially expressed proteins of obtained sperm were identified by the Sequential Window Acquisition of All Theoretical Mass Spectra Mass Spectrometry (SWATH-MS) and performed GO and KEGG analysis. RESULTS: A total of 2186 proteins were identified and 1591 proteins were quantified, of which 252 proteins were identified as differentially expressed proteins, including 124 upregulated and 128 downregulated. These differentially expressed proteins were involved in metabolic pathways, replication/recombination/repair, acrosomal vesicles, kinase regulators, fertilization, tyrosine metabolism, etc. Western blotting results showed that the expression levels of RAD23B and DFFA proteins and the levels of posttranslational ubiquitination and acetylation modifications in the experimental group were significantly higher than those in the control group, which was consistent with the results of proteomics analysis. CONCLUSIONS: Proteomic markers of sperm with high DNA fragmentation can be identified by the SWATH-MS and bioinformatic analysis, and new protein markers and posttranslational modifications related to sperm DNA damage are expected to be intensively explored. Our findings may improve our understanding of the basic molecular mechanism of sperm DNA damage.

Exploring the limits of metasurface polarization multiplexing capability based on deep learning.

Metasurfaces provide a new approach for planar optics and thus have realized multifunctional meta-devices with different multiplexing strategies, among which polarization multiplexing has received much attention due to its convenience. At present, a variety of design methods of polarization multiplexed metasurfaces have been developed based on different meta-atoms. However, as the number of polarization states increases, the response space of meta-atoms becomes more and more complex, and it is difficult for these methods to explore the limit of polarization multiplexing. Deep learning is one of the important routes to solve this problem because it can realize the effective exploration of huge data space. In this work, a design scheme for polarization multiplexed metasurfaces based on deep learning is proposed. The scheme uses a conditional variational autoencoder as an inverse network to generate structural designs and combines a forward network that can predict meta-atoms' responses to improve the accuracy of designs. The cross-shaped structure is used to establish a complicated response space containing different polarization state combinations of incident and outgoing light. The multiplexing effects of the combinations with different numbers of polarization states are tested by utilizing the proposed scheme to design nanoprinting and holographic images. The polarization multiplexing capability limit of four channels (a nanoprinting image and three holographic images) is determined. The proposed scheme lays the foundation for exploring the limits of metasurface polarization multiplexing capability.

Investigation of the mechanisms leading to human sperm DNA damage based on transcriptome analysis by RNA-seq techniques.

RESEARCH QUESTION: What are the molecular mechanisms leading to human sperm DNA damage? DESIGN: Semen samples were collected and the sperm DNA fragmentation index (DFI) was assessed. Differentially expressed RNA in spermatozoa with a high (DFI ≥30%, experimental group) or normal (DFI <30%, control group) DFI were identified by RNA-sequencing (RNA-seq) technology, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was performed. Three differentially expressed RNA related to sperm DNA damage and repair, namely PMS1, TP53BP1 and TLK2, were validated using real-time quantitative (RT-qPCR). RESULTS: A total of 19,970 expressed RNA were detected in the two groups. Compared with the control group, the expression levels of 189 RNA in the experimental group were significantly increased and those of 163 genes decreased. Gene Ontology enrichment analysis showed that these RNA were mainly concentrated in the ATPase-dependent transmembrane transport complex, extracellular exosome, somatic cell DNA recombination, protein binding, cytoplasm and regulation of localization. KEGG pathway analysis showed that these RNA were mainly related to the PI3K-Akt signalling pathway, endocytosis, p53 signalling pathway and cGMP-PKG signalling pathway. The RT-qPCR results showed that the expression levels of PMS1, TP53BP1 and TLK2 in the experimental group were significantly lower than in the control group (P = 0.01, 0.015 and 0.004, respectively), which was identical to the results of RNA sequencing. CONCLUSIONS: Differentially expressed RNA related to sperm DNA damage and repair may be identified by RNA-seq technology, which provides new insights into the understanding of sperm DNA damage and repair, and will help to discover new biomarkers related to sperm DNA damage.

Functionalized Fullerene Potentially Inhibits SARS-CoV-2 Infection by Modulating Spike Protein Conformational Changes.

The disease of SARS-CoV-2 has caused considerable morbidity and mortality globally. Spike proteins on the surface of SARS-CoV-2 allow it to bind with human cells, leading to infection. Fullerenes and their derivatives are promising SARS-CoV-2 inhibitors and drug-delivery vehicles. In this study, Gaussian accelerated molecular dynamics simulations and the Markov state model were employed to delve into the inhibitory mechanism of Fullerene-linear-polyglycerol-b-amine sulfate (F-LGPS) on spike proteins. During the study, it was discovered that fullerene derivatives can operate at the interface of the receptor-binding domain (RBD) and the N-terminal domain (NTD), keeping structural domains in a downward conformation. It was also observed that F-LGPS demonstrated superior inhibitory effects on the XBB variant in comparison to the wild-type variant. This study yielded invaluable insights for the potential development of efficient therapeutics targeting the spike protein of SARS-CoV-2.

Probing the Mechanisms of Inhibitors Binding to Presenilin Homologue Using Molecular Dynamics Simulations.

γ-secretase is an intramembrane proteolytic enzyme that is mainly involved in the cleavage and hydrolysis of the amyloid precursor (APP). The catalytic subunit presenilin 1 (PS1) is the catalytic subunit of γ-secretase. Since it was found that PS1 is responsible for Aß-producing proteolytic activity, which is involved in Alzheimer's disease, it is believed that reducing the activity of PS1 and preventing or delaying the production of Aß could help treat Alzheimer's disease. Consequently, in recent years, researchers have begun investigating the potential clinical efficacy of PS1 inhibitors. Currently, most PS1 inhibitors are only used as a tool to study the structure and function of PS1, and a few inhibitors with a high selectivity have been tested in clinics. Less-selective PS1 inhibitors were found to not only inhibit Aß production but also inhibit Notch cleavage, which led to serious adverse events. The archaeal presenilin homologue (PSH) is a surrogate protease of presenilin that is useful for agent screening. In this study, we performed 200 ns molecular dynamics simulations (MD) of four systems to explore the conformational changes of different ligands binding to PSH. Our results indicated that the PSH-L679 system formed 3-10 helices in TM4, loosening up TM4 and allowing substrates to enter the catalytic pocket, thereby making it less inhibitory. Additionally, we found that III-31-C can bring TM4 and TM6 closer, resulting in the contraction of the PSH active pocket. Altogether, these results provide the basis for the potential design of newer PS1 inhibitors.

Exploration of the Product Specificity of chitosanase CsnMY002 and Mutants Using Molecular Dynamics Simulations.

Chitosanase CsnMY002 is a new type of enzyme isolated from Bacillus subtilis that is used to prepare chitosan oligosaccharide. Although mutants G21R and G21K could increase Chitosan yield and thus increase the commercial value of the final product, the mechanism by which this happens is not known. Herein, we used molecular dynamics simulations to explore the conformational changes in CsnMY002 wild type and mutants when they bind substrates. The binding of substrate changed the conformation of protein, stretching and deforming the active and catalytic region. Additionally, the mutants caused different binding modes and catalysis, resulting in different degrees of polymerization of the final Chitooligosaccharide degradation product. Finally, Arg37, Ile145 ~ Gly148 and Trp204 are important catalytic residues of CsnMY002. Our study provides a basis for the engineering of chitosanases.

[Clinical observation of dapoxetine combined with percutaneous neuromuscular electrical stimulation in the treatment of primary premature ejaculation].

OBJECTIVE: To investigate the clinical efficacy of dapoxetine combined with transcutaneous neuromuscular electrical stimulation (TNES) in the treatment of primary premature ejaculation. METHODS: A total of 60 patients who met the diagnostic criteria for primary premature ejaculation were selected as study subjects and randomly divided into a dapoxetine group (control group) and a dapoxetine combined with percutaneous neuromuscular electrical stimulation group (observation group).30 patients in each group were treated for 4 weeks. Intravaginal ejaculatory latency time (IELT), the score of Premature Ejaculation Diagnostic Tool (PEDT), sympathetic skin response located in the penis (PSSR), Patient Health Questionnaire (PHQ-9), and Generalized Anxiety Disorder Questionnaire (GAD-7) before and after treatment were recorded in the two groups. Before and after treatment, the difference in observed indexes in the two groups and the comparison of effective rates between the two groups were analyzed. RESULTS: The latency of IELT and PSSR was prolonged and the PEDT score was decreased in both the observation group and the control group, the difference was statistically significant (P<0.01). Compared with the control group, the observation group had statistically significant differences in extending IELT and PSSR latency and reducing PEDT score (P<0.05). The effective rates of the observation group and control group were 90% and 63.33%, respectively, and the difference was statistically significant (P<0.05). There was no significant difference in the improvement of depression and anxiety levels between the two groups (P> 0.05). CONCLUSION: Dapoxetine combined with TNES has a better clinical effect than dapoxetine alone in the treatment of primary premature ejaculation, and can be used as an effective option for clinical treatment of primary premature ejaculation.

Influence of Individual-technology-task-environment Fit on University Student Online Learning Performance: The Mediating Role of Behavioral, Emotional, and Cognitive Engagement.

The present study aimed to examine whether and to what extent university student online learning performance was influenced by individual-technology fit (ITF), task-technology fit (TTF), environment-technology fit (ETF), and whether the influence was mediated by their behavioral, emotional, and cognitive engagement. A theoretical research model was developed by integrating the extended TTF theory and student engagement framework. The validity of the model was assessed using a partial least squares structural equation modeling approach based on data collected from 810 university students. Student learning performance was influenced by TTF (ß = 0.25, p < 0.001), behavioral engagement (ß = 0.25, p < 0.001), and emotional engagement (ß = 0.27, p < 0.001). Behavioral engagement was affected by TTF (ß = 0.31, p < 0.001) and ITF (ß = 0.41, p < 0.001). TTF, ITF, and ETF were observed as significant antecedents of emotional engagement (ß = 0.49, p < 0.001; ß = 0.19, p < 0.001; ß = 0.12, p = 0.001, respectively) and cognitive engagement (ß = 0.28, p < 0.001; ß = 0.34, p < 0.001; ß = 0.16, p < 0.001, respectively). Behavioral and emotional engagement served as mediators between fit variables and learning performance. We suggest the need for an extension to the TTF theory by introducing ITF and ETF dimensions and demonstrate the important role of these fit variables in facilitating student engagement and learning performance. Online education practitioners should carefully consider the fit between the individual, task, environment, and technology to facilitate student learning outcomes.

A Combination therapy using an mTOR inhibitor and Honokiol effectively induces autophagy through the modulation of AXL and Rubicon in renal cancer cells and restricts renal tumor growth following organ transplantation.

Development of cancer, including renal cancer, is a major problem in immunosuppressed patients. The mTOR inhibitor Rapamycin (RAPA) is used as an immunosuppressive agent in patients with organ transplants and other immunological disorders; and it also has antitumorigenic potential. However, long-term use of RAPA causes reactivation of Akt, and ultimately leads to enhanced tumor growth. Honokiol (HNK) is a natural compound, which possesses both anti-inflammatory and antitumorigenic properties. In this study, we investigated the effect of a novel combination therapy using RAPA + HNK on allograft survival and post-transplantation renal tumor growth. We observed that it effectively modulated the expression of some key regulatory molecules (like Carabin, an endogenous Ras inhibitor; and Rubicon, a negative regulator of autophagy) that play important roles in tumor cell growth and survival. This combination induced toxic autophagy and apoptosis to promote cancer cell death; and was associated with a reduced expression of the tumor-promoting receptor tyrosine kinase AXL. Finally, we utilized a novel murine model to examine the effect of RAPA + HNK on post-transplantation renal tumor growth. The combination treatment prolonged the allograft survival and significantly inhibited post-transplantation tumor growth. It was associated with reduced tumor expression of Rubicon and the cytoprotective/antioxidant heme oxygenase-1 to overcome therapeutic resistance. It also downregulated the coinhibitory programmed death-1 ligand, which plays major role(s) in the immune escape of tumor cells. Together, this combination treatment has a great potential to restrict renal tumor growth in transplant recipients as well as other immunosuppressed patients.

The Diagnostic Role of Neurophysiological Tests for Premature Ejaculation: A Prospective Multicenter Study.

PURPOSE: Premature ejaculation (PE) is one of the most common male sexual dysfunctions. Local anesthetics (LAs) and dapoxetine are frequently used to treat PE; however, previous studies show variable efficacy. This study aims to determine the efficacy of LAs and dapoxetine using a novel classification based on neurophysiological tests. MATERIALS AND METHODS: This multicenter cohort study enrolled adult men (568) with an intravaginal ejaculatory latency time (IELT) ≤2 minutes. Patients were divided into 4 groups according to the results of neurophysiological tests and assigned different treatments for 12 weeks: 1) penile sensory hyperexcitability type (Sens)-LAs; 2) penile sympathetic hyperexcitability type (Symp)-dapoxetine; 3) mixed type (Mixed)-both LAs and dapoxetine; 4) normal type (Norm)-both LAs and dapoxetine. Self-estimated IELT and patient-reported outcomes were recorded. RESULTS: The total percentage of men achieving IELT >2 minutes and ≥5 minutes after treatment were 82.7% and 76.7%, respectively. For men with abnormal results of neurophysiological tests, 401 (86.6%) had improved IELT >2 minutes after the 12-week treatment course, in which 375 (81.0%) achieved IELT ≥5 minutes. All patient-reported outcome measures improved in each group after 12 weeks of treatment, with greater improvements among those with abnormal neurophysiological tests. CONCLUSIONS: The efficacy of LAs and dapoxetine increased in PE patients with abnormal results of neurophysiological tests. This novel classification of PE using neurophysiological tests could help guide and improve efficacy of PE therapies.

Clustering Analysis, Structure Fingerprint Analysis, and Quantum Chemical Calculations of Compounds from Essential Oils of Sunflower (Helianthus annuus L.) Receptacles.

Sunflower (Helianthus annuus L.) is an appropriate crop for current new patterns of green agriculture, so it is important to change sunflower receptacles from waste to useful resource. However, there is limited knowledge on the functions of compounds from the essential oils of sunflower receptacles. In this study, a new method was created for chemical space network analysis and classification of small samples, and applied to 104 compounds. Here, t-SNE (t-Distributed Stochastic Neighbor Embedding) dimensions were used to reduce coordinates as node locations and edge connections of chemical space networks, respectively, and molecules were grouped according to whether the edges were connected and the proximity of the node coordinates. Through detailed analysis of the structural characteristics and fingerprints of each classified group, our classification method attained good accuracy. Targets were then identified using reverse docking methods, and the active centers of the same types of compounds were determined by quantum chemical calculation. The results indicated that these compounds can be divided into nine groups, according to their mean within-group similarity (MWGS) values. The three families with the most members, i.e., the d-limonene group (18), α-pinene group (10), and γ-maaliene group (nine members) determined the protein targets, using PharmMapper. Structure fingerprint analysis was employed to predict the binding mode of the ligands of four families of the protein targets. Thence, quantum chemical calculations were applied to the active group of the representative compounds of the four families. This study provides further scientific information to support the use of sunflower receptacles.

[Transcutaneous neuromuscular electrical stimulation for the treatment of erectile dysfunction: A clinical observation].

OBJECTIVE: To observe the clinical efficacy of transcutaneous neuromuscular electrical stimulation (TNES) in the treatment of ED. METHODS: According to the inclusion and exclusion criteria, we included and studied 25 cases of ED treated by TNES in Northern Jiangsu People's Hospital from June 2021 to February 2022 using the self-matched pre- and post-control method. Before and after treatment, we conducted RigiScan penile hardness test under audiovisual sexual stimulation (AVSS) for all the patients and obtained their scores on the Erectile Hardness Scale (EHS), IIEF-5, Premature Ejaculation Diagnostic Tool (PEDT), Patient Health Questionnaire 9 (PHQ-9) and Generalized Anxiety Disorder Questionnaire 7 (GAD-7). RESULTS: No adverse reactions such as pain, allergy, skin burn, and subcutaneous congestion were observed in any of the patients. There were statistically significant differences after treatment in erection time, average and maximum hardness of the penile tip, mean hardness of the penile root, and circumference of the penile tip (P < 0.05), but not in the circumference and maximum hardness of the penile root during erection compared with the baseline (P > 0.05). Significant improvement was observed after treatment in the IIEF-5 score (P < 0.05), with a total effectiveness rate of 68%, as well as in the PEDT score (P < 0.05) GAD-7 anxiety score (P < 0.05), but not in the PHQ-9 depression score (P > 0.05). CONCLUSION: TNES, as a safe and non-invasive therapy, can improve penile hardness under AVSS and the erectile function and anxiety symptoms of ED patients, and can be used as a new option for the treatment of ED.