Selective Modulators Research Articles

Deep Learning-Assisted Automatic Diagnosis of Anterior Cruciate Ligament Tear in Knee Magnetic Resonance Images.

Anterior cruciate ligament (ACL) tears are prevalent knee injures, particularly among active individuals. Accurate and timely diagnosis is essential for determining the optimal treatment strategy and assessing patient prognosis. Various previous studies have demonstrated the successful application of deep learning techniques in the field of medical image analysis. This study aimed to develop a deep learning model for detecting ACL tears in knee magnetic resonance Imaging (MRI) to enhance diagnostic accuracy and efficiency. The proposed model consists of three main modules: a Dual-Scale Data Augmentation module (DDA) to enrich the training data on both the spatial and layer scales; a selective group attention module (SG) to capture relationships across the layer, channel, and space scales; and a fusion module to explore the inter-relationships among various perspectives to achieve the final classification. To ensure a fair comparison, the study utilized a public dataset from MRNet, comprising knee MRI scans from 1250 exams, with a focus on three distinct views: axial, coronal, and sagittal. The experimental results demonstrate the superior performance of the proposed model, termed SGNET, in ACL tear detection compared with other comparison models, achieving an accuracy of 0.9250, a sensitivity of 0.9259, a specificity of 0.9242, and an AUC of 0.9747.

Total Synthesis of (15R)- and (15S)-Prostaglandin A2.

From both pharmaceutical and structural perspectives, the large family of prostaglandins represent a truly remarkable class of natural products. Prostaglandin A2 is a tissue hormone naturally found in human seminal plasma and in the sea whip Plexaura homomalla with yet poorly understood biological or therapeutic effects. Herein, a novel strategy for the stereoselective construction of both naturally occurring prostaglandin A2 epimers and first insights into their functional effects on the major inhibitory neurotransmitter γ-aminobutyric acid (GABA) type A receptors (GABAAR) are provided. The synthesis of both epimers was achieved in only 11 steps starting from commercially available 2,5-dimethoxy-tetrahydrofuran employing an organocatalytic domino-aldol reaction, a Mizoroki-Heck reaction, a Wittig reaction as well as an oxidation-decarboxylation sequence. The (15R)-epimer significantly reduced GABA-induced currents through GABAA receptors while its (15S)-epimer did not show any significant effect. These data suggest that (15R)-PGA2 might serve as a novel scaffold for the development of selective GABAA receptor modulators.

A therapeutic small molecule enhances γ-oscillations and improves cognition/memory in Alzheimer’s disease model mice

Brain rhythms provide the timing for recruitment of brain activity required for linking together neuronal ensembles engaged in specific tasks. The γ-oscillations (30 to 120 Hz) orchestrate neuronal circuits underlying cognitive processes and working memory. These oscillations are reduced in numerous neurological and psychiatric disorders, including early cognitive decline in Alzheimer's disease (AD). Here, we report on a potent brain-permeable small molecule, DDL-920 that increases γ-oscillations and improves cognition/memory in a mouse model of AD, thus showing promise as a class of therapeutics for AD. We employed anatomical, in vitro and in vivo electrophysiological, and behavioral methods to examine the effects of our lead therapeutic candidate small molecule. As a novel in central nervous system pharmacotherapy, our lead molecule acts as a potent, efficacious, and selective negative allosteric modulator of the γ-aminobutyric acid type A receptors most likely assembled from α1β2δ subunits. These receptors, identified through anatomical and pharmacological means, underlie the tonic inhibition of parvalbumin (PV) expressing interneurons (PV+INs) critically involved in the generation of γ-oscillations. When orally administered twice daily for 2 wk, DDL-920 restored the cognitive/memory impairments of 3- to 4-mo-old AD model mice as measured by their performance in the Barnes maze. Our approach is unique as it is meant to enhance cognitive performance and working memory in a state-dependent manner by engaging and amplifying the brain's endogenous γ-oscillations through enhancing the function of PV+INs.

Dynamic methylation and expression of alternative promoters for oestrogen receptor alpha in cell line models of fulvestrant resistance.

Oestrogen receptor alpha (ER; gene symbol ESR1) is the most important prognostic and treatment-predictive biomarker in breast cancer. Drugs targeting oestrogen and ER for endocrine therapy of breast cancer include aromatase inhibitors, the selective ER modulator tamoxifen and the selective ER degrader fulvestrant. Tumours can develop resistance to endocrine therapy through several mechanisms, which is often linked to altered expression of ER. To investigate the role of promoter methylation in the regulation of ESR1 expression, we used bisulfite sequencing to measure methylation at CpG sites in alternative ER promoter regions for six cell line models of fulvestrant resistance. Both CpG methylation and expression of alternative first exons changed dynamically, with striking differences between cell lines that had stable or unstable resistance upon fulvestrant withdrawal. Methylation at some CpG sites was strongly negatively correlated with expression of specific first exons. In a breast tumour cohort, higher relative expression of upstream alternative first exons was associated with worse prognosis in post-menopausal women with ER-positive tumours who received endocrine therapy.

Medical Image Classification with a Hybrid SSM Model Based on CNN and Transformer

Medical image classification, a pivotal task for diagnostic accuracy, poses unique challenges due to the intricate and variable nature of medical images compared to their natural counterparts. While Convolutional Neural Networks (CNNs) and Transformers are prevalent in this domain, each architecture has its drawbacks. CNNs, despite their strength in local feature extraction, fall short in capturing global context, whereas Transformers excel at global information but can overlook fine-grained details. The integration of CNNs and Transformers in a hybrid model aims to bridge this gap by enabling simultaneous local and global feature extraction. However, this approach remains constrained in its capacity to model long-range dependencies, thereby hindering the efficient extraction of distant features. To address these issues, we introduce the MambaConvT model, which employs a state-space approach. It begins by locally processing input features through multi-core convolution, enhancing the extraction of deep, discriminative local details. Next, depth-separable convolution with a 2D selective scanning module (SS2D) is employed to maintain a global receptive field and establish long-distance connections, capturing the fine-grained features. The model then combines hybrid features for comprehensive feature extraction, followed by global feature modeling to emphasize on global detail information and optimize feature representation. This paper conducts thorough performance experiments on different algorithms across four publicly available datasets and two private datasets. The results demonstrate that MambaConvT outperforms the latest classification algorithms in terms of accuracy, precision, recall, F1 score, and AUC value ratings, achieving superior performance in the precise classification of medical images.

Modulation of Selectivity and Activity of Ni/TiO2 Catalysts by Metal Dispersion in Photothermal CO2 Hydrogenation

Ni/TiO2 catalysts are efficient and cost‐effective for photothermal CO2 hydrogenation. However, the achieved CO/CH4 ratio strongly depends on the specific characteristics of these catalysts. To further ascertain the role of metal dispersion and photoactivation on selectivity, in this work we investigate the impact of Ni loading over high surface area anatase on the photothermal performance. Catalysts with 3 and 10 wt. % of Ni prepared by incipient wetness impregnation show initial good dispersion of the metal, although after activation in H2 metallic Ni nanoparticles are observed for 10%Ni/TiO2. This last catalyst demonstrates superior CO2 hydrogenation activity at high temperature, but below 200 ºC it is overpassed by the catalyst with 3 wt % Ni/TiO2. The selectivity varies remarkably with Ni loadings. Thus, at 350 °C about 93 % of methane is obtained over 10 wt.% Ni/TiO2, while 3%Ni/TiO2 yields about 97 % of CO. Low‐intensity UV irradiation enhances performance, particularly at temperatures below 200 ºC, where an increment in the production of methane of up to 75 % is observed for 3 wt.% Ni/TiO2 at 200 ºC. These results highlight the influence of metal dispersion, along with irradiation on modulating the selectivity of the photothermal CO2 hydrogenation

Efficacy and Safety of Pemafibrate, a Novel Selective PPARα Modulator in Chinese Patients with Dyslipidemia: A Double-Masked, Randomized, Placebo- and Active-Controlled Comparison Trial.

Pemafibrate substantially lowers serum triglyceride (TG) levels and increases high-density lipoprotein cholesterol (HDL-C) levels primarily in Japan, but it has not been evaluated in China. We aimed to confirm the efficacy and safety of pemafibrate in Chinese patients with hypertriglyceridemia and low HDL-C levels by comparing placebo and fenofibrate. A multicenter, double-masked trial was conducted in China involving 344 patients with high TG and low HDL-C levels randomly assigned to one of four groups: pemafibrate 0.2 mg/d, pemafibrate 0.4 mg/d, fenofibrate 200 mg/d, or placebo for 12 weeks. The primary endpoint was the percentage change in fasting TG levels. The percentage change in TG levels from baseline was -34.1%, -44.0%, -30.5%, and 6.5% in the pemafibrate 0.2 mg/d, pemafibrate 0.4 mg/d, fenofibrate 200 mg/d, and placebo groups, respectively. Pemafibrate 0.4 mg/d significantly reduced TG levels compared with that in both placebo (p＜0.0001) and fenofibrate groups (p=0.0083). Significant improvements in HDL-C, remnant cholesterol, and apolipoprotein A1 levels were also observed with both doses of pemafibrate than with the placebo. Pemafibrate showed significantly smaller changes in alanine aminotransferase, aspartate aminotransferase, and serum creatinine levels than those with fenofibrate. In Chinese patients, pemafibrate exhibited superior efficacy in improving TG levels and enhanced hepatic and renal safety compared to fenofibrate. Thus, pemafibrate may represent a promising therapeutic option for dyslipidemia in Chinese patients.

A comprehensive gas chromatography electron ionization high resolution mass spectrometry study of a new steroidal selective androgen receptor modulator (SARM) compound S42.

The synthetic 20-keto-steroid S42 (1) demonstrated selective androgen receptor modulator (SARM) properties in preclinical studies and, consequently, received growing attention also in the context of sports drug testing programs. Fundamental understanding of the behavior of S42 (1) and of relevant derivatives in gas chromatography-electron ionization MS experiments at high resolution (GC-EI-HRMS) is indispensable to develop a reliable qualitative and quantitative doping control method for S42 (1) and its metabolites in body fluid matrices. We present important fundamental mechanistic data on the EI fragmentation behavior of S42 (1) and of silyl ether derivatives as well as of stable isotope-labelled reference material.

A kirigami-based reconfigurable metasurface for selective electromagnetic transmission modulation

Tunable three-dimensional (3D) electromagnetic metasurfaces are essential for achieving selective modulation of polarized waves, but they usually require complex designs and the use of smart materials, posing great implementation challenges. Here, we propose a novel kirigami-based reconfigurable electromagnetic metasurface, which consists of a kirigami-based deformable thin polyimide substrate and periodically arranged copper split-ring resonators. By simple stretch, the two-dimensional (2D) planar metasurface can be uniformly transformed into a 3D state, enabling it to effectively and selectively modulate linearly and circularly polarized waves. Experimental and numerical results reveal the mechanical deformation and transmission characteristics of the metasurface under applied strains. It is shown that the metasurface exhibits good selective transmission tunability while the resonant frequency remains basically unchanged for both transverse electric and transverse magnetic polarized waves. Furthermore, the selective tuning mechanism and the influence of geometrical parameters are also illustrated by the equivalent circuit analysis.

Joint PAPR reduction and channel estimation in low-complexity VLC systems

In recent times, there has been a surge in interest surrounding visible light communication (VLC) as a compelling adjunct to radio frequency (RF) technology. This enthusiasm stems from deployments showcasing its efficacy in high-speed data transmission, characterized by low complexity, robust security, and high reliability. Among the variants of orthogonal frequency division multiplexing (OFDM), direct current-biased optical OFDM (DCO-OFDM) stands out in the realm of VLC systems, owing to its ability to furnish rapid data provisioning. However, a significant drawback of selective mapping (SLM) based DCO-OFDM transceivers lies in its computational complexity and practical implementation challenges. To address this limitation, this paper presents a modified SLM technique termed selective phase modulation (SPM) aimed at mitigating the peak-to-average power ratio (PAPR) in the transmitter section while simplifying the data decoding process at the receiver end. The SPM method utilizes a phase modulation and demodulation process based on clusters and assisted by pilot signals, streamlining the transceiver architecture. The SPM methodology offers reduced computational complexity at the transmitter by selectively applying phase sequences to data, circumventing the necessity for side information (SI) transmission and SI estimation at the receiver with a known pilot symbol inserted in the data. This dual-functionality mechanism not only achieves complexity reduction but also facilitates channel estimation at the receiver for proper data recovery. Based on complexity analysis and simulation outcomes, the computational complexity reduction strategy outperforms the conventional selective mapping (CSLM) technique, VLC-based pilot-assisted PAPR reduction systems, and embedded coded modulation (ECM). This allows for smooth incorporation of this technology into future VLC systems.

Drug-drug interactions between pemafibrate and statins on pharmacokinetics in healthy male volunteers: Open-label, randomized, 6-sequence, 3-period crossover studies.

Elevated triglyceride levels are associated with an increased risk of cardiovascular events despite guideline-based statin treatment of low-density lipoprotein cholesterol. Peroxisome proliferator-activated receptor α (PPARα) agonists exert a significant triglyceride-lowering effect. However, combination therapy of PPARα agonists with statins poses an increased risk of rhabdomyolysis, which is rare but a major concern of the combination therapy. Pharmacokinetic interaction is suspected to be a contributing factor to the risk. To examine the potential for combination therapy with the selective PPARα modulator (SPPARMα) pemafibrate and statins, drug-drug interaction studies were conducted with open-label, randomized, 6-sequence, 3-period crossover designs for the combination of pemafibrate 0.2 mg twice daily and each of 6 statins once daily: pitavastatin 4 mg/day (n = 18), atorvastatin 20 mg/day (n = 18), rosuvastatin 20 mg/day (n = 29), pravastatin 20 mg/day (n = 18), simvastatin 20 mg/day (n = 20), and fluvastatin 60 mg/day (n = 19), involving healthy male volunteers. The pharmacokinetic parameters of pemafibrate and each of the statins were similar regardless of coadministration. There was neither an effect on the systemic exposure of pemafibrate nor a clinically important increase in the systemic exposure of any of the statins on the coadministration although the systemic exposure of simvastatin was reduced by about 15% and its open acid form by about 60%. The HMG-CoA reductase inhibitory activity in plasma samples from the simvastatin and pemafibrate combination group was about 70% of that in the simvastatin alone group. In conclusion, pemafibrate did not increase the systemic exposure of statins, and vice versa, in healthy male volunteers.

Structure-based screening, optimization and biological evaluation of novel chrysin-based derivatives as selective PPARγ modulators for the treatment of T2DM and hepatic steatosis

In consideration of several serious side effects induced by the classical AF-2 involved “lock” mechanism, recently disclosed PPARγ-Ser273 phosphorylation mode of action has become an alternative and mainstream mechanism for currently PPARγ-based drug discovery and development with an improved therapeutic index. In this study, by virtue of structure-based virtual high throughput screening (SB-VHTS), structurally chemical optimization by targeting the inhibition of the PPARγ-Ser273 phosphorylation as well as in vitro biological evaluation, which led to the final identification of a chrysin-based potential hit (YGT-31) as a novel selective PPARγ modulator with potent binding affinity and partial agonism. Further in vivo evaluation demonstrated that YGT-31 possessed potent glucose-lowering and relieved hepatic steatosis effects without involving the TZD-associated side effects. Mechanistically, YGT-31 presented such desired therapeutic index, mainly because it effectively inhibited the CDK5-mediated PPARγ-Ser273 phosphorylation, selectively elevated the level of insulin sensitivity-related Glut4 and adiponectin but decreased the expression of insulin-resistance-associated genes PTP1B and SOCS3 as well as inflammation-linked genes IL-6, IL-1β and TNFα. Finally, the molecular docking study was also conducted to uncover an interesting hydrogen-bonding network of YGT-31 with PPARγ-Ser273 phosphorylation-related key residues Ser342 and Glu343, which not only gave a clear verification for our targeting modification but also provided a proof of concept for the abovementioned molecular mechanism.

Effects of Clinical Mutations in the Second Coordination Sphere and Remote Regions on the Catalytic Mechanism of Non-Heme Fe(II)/2-Oxoglutarate-Dependent Aspartyl Hydroxylase AspH.

Aspartyl/asparaginyl hydroxylase (AspH) catalyzes the post-translational hydroxylations of vital human proteins, playing an essential role in maintaining their biological functions. Single-point mutations in the Second Coordination Sphere (SCS) and long-range (LR) residues of AspH have been linked to pathological conditions such as the ophthalmologic condition Traboulsi syndrome and chronic kidney disease (CKD). Although the clinical impacts of these mutations are established, there is a critical knowledge gap regarding their specific atomistic effects on the catalytic mechanism of AspH. In this study, we report integrated computational investigations on the potential mechanistic implications of four mutant forms of human AspH with clinical importance: R735W, R735Q, R688Q, and G434V. All the mutant forms exhibited altered binding interactions with the co-substrate 2-oxoglutarate (2OG) and the main substrate in the ferric-superoxo and ferryl complexes, which are critical for catalysis, compared to the wild-type (WT). Importantly, the mutations strongly influence the energetics of the frontier molecular orbitals (FMOs) and, thereby, the activation energies for the hydrogen atom transfer (HAT) step compared to the WT AspH. Insights from our study can contribute to enzyme engineering and the development of selective modulators for WT and mutants of AspH, ultimately aiding in treating cancers, Traboulsi syndrome and, CKD.

Proof-of-principle demonstration of free space semi-quantum key distribution based on the single-state protocol

Semi-quantum key distribution (SQKD) allows a quantum user and a classical user to share a string of secret keys, providing support for application scenarios that cannot withstand the high cost of quantum resources. In this paper, we propose what we believe to be the first proof-of-principle experimental demonstration of free space SQKD based on the single-state protocol, which is equipped with polarization encoding scheme employing the method of selective modulation. During the half-hour test time for each operation, the overall experiment obtained the original key rate of 107.2 kbps at the repetition frequency of 100 MHz, and the average bit error rate was determined to be 1.65% for CTRL operation and 0.64% for SIFT operation. The experimental results indicate that our system exhibits commendable performance and stability at a low bit error rate that represents a significant initial stride toward the future practical deployment of SQKD systems within free-space channels.

Self-Supervised Contrastive Learning for Automated Segmentation of Brain Tumor MRI Images in Schizophrenia

Schizophrenic patients’ brain tumor magnetic resonance imaging (MRI) images are important references for doctors to diagnose and treat schizophrenia. However, automatic segmentation of these images is a professional and tedious task. Existing methods suffer from problems such as large model parameters, long computation time, and inadequate image processing. To achieve more accurate segmentation of brain tumors, we propose brain tumor MRI images for automatic segmentation using self-supervised contrastive learning in schizophrenia patients (BTCSSSP). First, a denoising algorithm based on progressive principal component analysis approximation and adaptive clustering is designed to process the noisy MRI images. Second, a brightness-aware image enhancement algorithm is developed to address the problems of non-uniformity, unclear boundaries, and poor spatial resolution of the MRI images. Finally, a cross-scale U-Net network with selective feature fusion attention module is designed based on self-supervised contrastive learning to achieve automatic segmentation of brain tumor MRI images. The results show that the BTCSSSP method yields higher Recall and Precision than existing methods. The maximum recall is 95%, and the image segmentation precision is 95%, thus indicating good practical applicability.

Neuraminidase-1 (NEU1): Biological Roles and Therapeutic Relevance in Human Disease.

Neuraminidases catalyze the desialylation of cell-surface glycoconjugates and play crucial roles in the development and function of tissues and organs. In both physiological and pathophysiological contexts, neuraminidases mediate diverse biological activities via the catalytic hydrolysis of terminal neuraminic, or sialic acid residues in glycolipid and glycoprotein substrates. The selective modulation of neuraminidase activity constitutes a promising strategy for treating a broad spectrum of human pathologies, including sialidosis and galactosialidosis, neurodegenerative disorders, cancer, cardiovascular diseases, diabetes, and pulmonary disorders. Structurally distinct as a large family of mammalian proteins, neuraminidases (NEU1 through NEU4) possess dissimilar yet overlapping profiles of tissue expression, cellular/subcellular localization, and substrate specificity. NEU1 is well characterized for its lysosomal catabolic functions, with ubiquitous and abundant expression across such tissues as the kidney, pancreas, skeletal muscle, liver, lungs, placenta, and brain. NEU1 also exhibits a broad substrate range on the cell surface, where it plays hitherto underappreciated roles in modulating the structure and function of cellular receptors, providing a basis for it to be a potential drug target in various human diseases. This review seeks to summarize the recent progress in the research on NEU1-associated diseases and highlight the mechanistic implications of NEU1 in disease pathogenesis. An improved understanding of NEU1-associated diseases should help accelerate translational initiatives to develop novel or better therapeutics.

SFPL: Sample-specific fine-grained prototype learning for imbalanced medical image classification

Imbalanced classification is a common and difficult task in many medical image analysis applications. However, most existing approaches focus on balancing feature distribution and classifier weights between classes, while ignoring the inner-class heterogeneity and the individuality of each sample. In this paper, we proposed a sample-specific fine-grained prototype learning (SFPL) method to learn the fine-grained representation of the majority class and learn a cosine classifier specifically for each sample such that the classification model is highly tuned to the individual’s characteristic. SFPL first builds multiple prototypes to represent the majority class, and then updates the prototypes through a mixture weighting strategy. Moreover, we proposed a uniform loss based on set representations to make the fine-grained prototypes distribute uniformly. To establish associations between fine-grained prototypes and cosine classifier, we propose a selective attention aggregation module to select the effective fine-grained prototypes for final classification. Extensive experiments on three different tasks demonstrate that SFPL outperforms the state-of-the-art (SOTA) methods. Importantly, as the imbalance ratio increases from 10 to 100, the improvement of SFPL over SOTA methods increases from 2.2% to 2.4%; as the training data decreases from 800 to 100, the improvement of SFPL over SOTA methods increases from 2.2% to 3.8%.

Effect of selective androgen receptor modulator RAD140 on prostate and testosterone levels in Wistar strain rats with bilateral orchidectomy

BACKGROUND Selective androgen receptor modulators (SARMs) have been investigated as a potential treatment for hypogonadism, a condition characterized by low testosterone levels in men. The idea is to provide therapeutic benefits similar to traditional testosterone replacement therapy. However, research in this area is still in its early stages, and more extensive studies are needed to establish the efficacy of SARM. This study aimed to determine the impact of SARM RAD140 on testosterone levels, fibromuscular stroma, and prostate mass in rats undergoing bilateral orchidectomy. METHODS This was an in vivo study using posttest-only control group design in rats (Rattus norvegicus). The positive and negative control groups consisted of rats with and without bilateral orchidectomy, respectively. The treatment groups were rats given SARM RAD140 with and without orchidectomy. Testosterone levels, histopathology, and prostate mass were examined at the end of week 6, and the quantitative data were analyzed using one-way ANOVA. RESULTS This study found no difference in prostate mass (0.598 [0.05] g versus 0.590 [0.07] g, p = 0.984), fibromuscular stroma ratio (0.483 [0.094] versus 0.463 [0.057], p = 0.984), and testosterone level (0.006 [0.005] ng/dl versus 0.014 [0.004] ng/dl, p = 0.098) compared to positive control with orchidectomy and SARM RAD140 administration 6 weeks after treatment. CONCLUSIONS There were no differences in testosterone levels, prostate mass, or the ratio of fibromuscular stroma to epithelium area in rats undergoing bilateral orchidectomy and placebo surgery with the administration of SARM RAD140.

Differential Interactions of Flavonoids with the Aryl Hydrocarbon Receptor In Silico and Their Impact on Receptor Activity In Vitro.

The molecular mechanisms underlying the observed anticancer effects of flavonoids remain unclear. Increasing evidence shows that the aryl hydrocarbon receptor (AHR) plays a crucial role in neoplastic disease progression, establishing it as a potential drug target. This study evaluated the potential of hydroxy flavonoids, known for their anticancer properties, to interact with AHR, both in silico and in vitro, aiming to understand the mechanisms of action and identify selective AHR modulators. A PAS-B domain homology model was constructed to evaluate in silico interactions of chrysin, naringenin, quercetin apigenin and agathisflavone. The EROD activity assay measured the effects of flavonoids on AHR's activity in human breast cancer cells (MCF7). Simulations showed that chrysin, apigenin, naringenin, and quercetin have the highest AHR binding affinity scores (-13.14 to -15.31), while agathisflavone showed low scores (-0.57 and -5.14). All tested flavonoids had the potential to inhibit AHR activity in a dose-dependent manner in the presence of an agonist (TCDD) in vitro. This study elucidates the distinct modulatory effects of flavonoids on AHR, emphasizing naringenin's newly described antagonistic potential. It underscores the importance of understanding flavonoid's molecular mechanisms, which is crucial for developing novel cancer therapies based on these molecules.

EEG subject-dependent neurofeedback training selectively impairs declarative memories consolidation process

The process of stabilization and storage of memories, known as consolidation, can be modulated by different interventions. Research has shown that self-regulation of brain activity through Neurofeedback (NFB) during the consolidation phase significantly impacts memory stabilization. While some studies have successfully modulated the consolidation phase using traditional EEG-based Neurofeedback (NFB) that focuses on general parameters, such as training a specific frequency band at particular electrodes, they often overlook the unique and complex neurodynamics that underlie each memory content in different individuals, potentially limiting the selective modulation of memories.The main objective of this study is to investigate the effects of a Subject-Dependent NFB (SD-NFB), based on individual models created from the brain activity of each participant, on long-term declarative memories. Participants underwent an experimental protocol involving three sessions. In the first session, they learned images of faces and houses while their brain activity was recorded. This EEG data was used to create individualized models to identify brain patterns related to learning these images. Participants were then divided into three groups, with one group receiving SD-NFB to enhance brain activity linked to faces, another to houses, and a CONTROL sham group that did not receive SD-NFB.Memory performance was evaluated 24 h and seven days later using an ‘old-new’ recognition task, where participants distinguished between ‘old’ and ‘new’ images. The results showed that memory contents (faces or houses) whose brain patterns were trained via SD-NFB scored lower in recognition compared to untrained contents, as evidenced 24 h and seven days post-training.In summary, this study demonstrates that SD-NFB can selectively impact the consolidation of specific declarative memories. This technique could hold significant implications for clinical applications, potentially aiding in the modulation of declarative memory strength in neuropsychiatric disorders where memories are pathologically exacerbated.