An epigenetic barrier sets the timing of human neuronal maturation.

The pace of human brain development is highly protracted compared with most other species1-7. The maturation of cortical neurons is particularly slow, taking months to years to develop adult functions3-5. Remarkably, such protracted timing is retained in cortical neurons derived from human pluripotent stem cells (hPSCs) during in vitro differentiation or upon transplantation into the mouse brain4,8,9. Those findings suggest the presence of a cell-intrinsic clock setting the pace of neuronal maturation, although the molecular nature of this clock remains unknown. Here we identify an epigenetic developmental programme that sets the timing of human neuronal maturation. First, we developed a hPSC-based approach to synchronize the birth of cortical neurons in vitro which enabled us to define an atlas of morphological, functional and molecular maturation. We observed a slow unfolding of maturation programmes, limited by the retention of specific epigenetic factors. Loss of function of several of those factors in cortical neurons enables precocious maturation. Transient inhibition of EZH2, EHMT1 and EHMT2 or DOT1L, at progenitor stage primes newly born neurons to rapidly acquire mature properties upon differentiation. Thus our findings reveal that the rate at which human neurons mature is set well before neurogenesis through the establishment of an epigenetic barrier in progenitor cells. Mechanistically, this barrier holds transcriptional maturation programmes in a poised state that is gradually released to ensure the prolonged timeline of human cortical neuron maturation.

Meta-DHGNN: method for CRS-related cytokines analysis in CAR-T therapy based on meta-learning directed heterogeneous graph neural network.

Chimeric antigen receptor T-cell (CAR-T) immunotherapy, a novel approach for treating blood cancer, is associated with the production of cytokine release syndrome (CRS), which poses significant safety concerns for patients. Currently, there is limited knowledge regarding CRS-related cytokines and the intricate relationship between cytokines and cells. Therefore, it is imperative to explore a reliable and efficient computational method to identify cytokines associated with CRS. In this study, we propose Meta-DHGNN, a directed and heterogeneous graph neural network analysis method based on meta-learning. The proposed method integrates both directed and heterogeneous algorithms, while the meta-learning module effectively addresses the issue of limited data availability. This approach enables comprehensive analysis of the cytokine network and accurate prediction of CRS-related cytokines. Firstly, to tackle the challenge posed by small datasets, a pre-training phase is conducted using the meta-learning module. Consequently, the directed algorithm constructs an adjacency matrix that accurately captures potential relationships in a more realistic manner. Ultimately, the heterogeneous algorithm employs meta-photographs and multi-head attention mechanisms to enhance the realism and accuracy of predicting cytokine information associated with positive labels. Our experimental verification on the dataset demonstrates that Meta-DHGNN achieves favorable outcomes. Furthermore, based on the predicted results, we have explored the multifaceted formation mechanism of CRS in CAR-T therapy from various perspectives and identified several cytokines, such as IFNG (IFN-γ), IFNA1, IFNB1, IFNA13, IFNA2, IFNAR1, IFNAR2, IFNGR1 and IFNGR2 that have been relatively overlooked in previous studies but potentially play pivotal roles. The significance of Meta-DHGNN lies in its ability to analyze directed and heterogeneous networks in biology effectively while also facilitating CRS risk prediction in CAR-T therapy.

EphB2-dependent prefrontal cortex activation promotes long-range social approach and partner responsiveness.

Social behavior starts with dynamic approach prior to the final consummation. The flexible processes ensure mutual feedback across social brains to transmit signals. However, how the brain responds to the initial social stimuli precisely to elicit timed behaviors remains elusive. Here, by using real-time calcium recording, we identify the abnormalities of EphB2 mutant with autism-associated Q858X mutation in processing long-range approach and accurate activity of prefrontal cortex (dmPFC). The EphB2-dependent dmPFC activation precedes the behavioral onset and is actively associated with subsequent social action with the partner. Furthermore, we find that partner dmPFC activity is responsive coordinately to the approaching WT mouse rather than Q858X mutant mouse, and the social defects caused by the mutation are rescued by synchro-optogenetic activation in dmPFC of paired social partners. These results thus reveal that EphB2 sustains neuronal activation in the dmPFC that is essential for the proactive modulation of social approach to initial social interaction.

SP1-stimulated miR-208a-5p aggravates sepsis-induced myocardial injury via targeting XIAP.

The development of sepsis can lead to many organ dysfunction and even death. Myocardial injury is one of the serious complications of sepsis leading to death. New evidence suggests that microRNAs (miRNAs) play a critical role in infection myocardial injury. However, the mechanism which miR-208a-5p regulates sepsis-induced myocardial injury remains unclear. To mimic sepsis-induced myocardial injury in vitro, rat primary cardiomyocytes were treated with LPS. Cell viability and apoptosis were tested by CCK-8 and flow cytometry, respectively. The secretion of inflammatory factors was analyzed by ELISA. mRNA and protein levels were detected by RT-qPCR and Western blotting. The interaction among SP1, XIAP and miR-208a-5p was detected using dual luciferase report assay. Ultrasonic analysis and HE staining was performed to observe the effect of miR-208a-5p in sepsis-induced rats. Our findings indicated that miR-208a-5p expression in primary rat cardiomyocytes was increased by LPS. MiR-208a-5p inhibitor reversed LPS-induced cardiomyocytes injury through inhibiting the apoptosis. Furthermore, the inflammatory injury in cardiomyocytes was induced by LPS, which was rescued by miR-208a-5p inhibitor. In addition, downregulation of miR-208a-5p improved LPS-induced sepsis myocardial injury in vivo. Mechanistically, XIAP might be a target gene of miR-208a-5p. SP1 promoted transcription of miR-208a by binding to the miR-208a promoter region. Moreover, silencing of XIAP reversed the regulatory of miR-208a-5p inhibitor on cardiomyocytes injury. To sum up, those findings revealed silencing of miR-208a-5p could alleviate sepsis-induced myocardial injury, which would grant a new process for the treatment of sepsis.

PrCRS: a prediction model of severe CRS in CAR-T therapy based on transfer learning.

BACKGROUND: CAR-T cell therapy represents a novel approach for the treatment of hematologic malignancies and solid tumors. However, its implementation is accompanied by the emergence of potentially life-threatening adverse events known as cytokine release syndrome (CRS). Given the escalating number of patients undergoing CAR-T therapy, there is an urgent need to develop predictive models for severe CRS occurrence to prevent it in advance. Currently, all existing models are based on decision trees whose accuracy is far from meeting our expectations, and there is a lack of deep learning models to predict the occurrence of severe CRS more accurately. RESULTS: We propose PrCRS, a deep learning prediction model based on U-net and Transformer. Given the limited data available for CAR-T patients, we employ transfer learning using data from COVID-19 patients. The comprehensive evaluation demonstrates the superiority of the PrCRS model over other state-of-the-art methods for predicting CRS occurrence. We propose six models to forecast the probability of severe CRS for patients with one, two, and three days in advance. Additionally, we present a strategy to convert the model's output into actual probabilities of severe CRS and provide corresponding predictions. CONCLUSIONS: Based on our findings, PrCRS effectively predicts both the likelihood and timing of severe CRS in patients, thereby facilitating expedited and precise patient assessment, thus making a significant contribution to medical research. There is little research on applying deep learning algorithms to predict CRS, and our study fills this gap. This makes our research more novel and significant. Our code is publicly available at https://github.com/wzy38828201/PrCRS . The website of our prediction platform is: http://prediction.unicar-therapy.com/index-en.html .

Graphical models for identifying pore-forming proteins.

Pore-forming toxins (PFTs) are proteins that form lesions in biological membranes. Better understanding of the structure and function of these proteins will be beneficial in a number of biotechnological applications, including the development of new pest control methods in agriculture. When searching for new pore formers, existing sequence homology-based methods fail to discover truly novel proteins with low sequence identity to known proteins. Search methodologies based on protein structures would help us move beyond this limitation. As the number of known structures for PFTs is very limited, it's quite challenging to identify new proteins having similar structures using computational approaches like deep learning. In this article, we therefore propose a sample-efficient graphical model, where a protein structure graph is first constructed according to consensus secondary structures. A semi-Markov conditional random fields model is then developed to perform protein sequence segmentation. We demonstrate that our method is able to distinguish structurally similar proteins even in the absence of sequence similarity (pairwise sequence identity < 0.4)-a feat not achievable by traditional approaches like HMMs. To extract proteins of interest from a genome-wide protein database for further study, we also develop an efficient framework for UniRef50 with 43 million proteins.

TGR5 signalling in heart and brain injuries: focus on metabolic and ischaemic mechanisms.

The heart and brain are the core organs of the circulation and central nervous system, respectively, and play an important role in maintaining normal physiological functions. Early neuronal and cardiac damage affects organ function. The relationship between the heart and brain is being continuously investigated. Evidence-based medicine has revealed the concept of the "heart- brain axis," which may provide new therapeutic strategies for certain diseases. Takeda protein-coupled receptor 5 (TGR5) is a metabolic regulator involved in energy homeostasis, bile acid homeostasis, and glucose and lipid metabolism. Inflammation is critical for the development and regeneration of the heart and brain during metabolic diseases. Herein, we discuss the role of TGR5 as a metabolic regulator of heart and brain development and injury to facilitate new therapeutic strategies for metabolic and ischemic diseases of the heart and brain.

Downregulation of N4-acetylcytidine modification in myeloid cells attenuates immunotherapy and exacerbates hepatocellular carcinoma progression.

BACKGROUND: N4-acetylcytidine (ac4C) is a conserved and abundant mRNA modification that controls protein expression by affecting translation efficiency and mRNA stability. Whether the ac4C modification of mRNA regulates hepatocellular carcinoma (HCC) development or affects the immunotherapy of HCC is unknown. METHODS: By constructing an orthotopic transplantation mouse HCC model and isolating tumour-infiltrated immunocytes, we evaluated the ac4C modification intensity using flow cytometry. Remodelin hydrobromide (REM), an ac4C modification inhibitor, was systematically used to understand the extensive role of ac4C modification in immunocyte phenotypes. Single-cell RNA-seq was performed to comprehensively evaluate the changes in the tumour-infiltrating immunocytes and identify targeted cell clusters. RNA-seq and RIP-seq analyses were performed to elucidate the underlying molecular mechanisms. Tyramide Signal Amplification (TSA) analysis on the HCC tissue microarray was performed to explore the clinical relatedness of our findings. RESULTS: Ac4C modification promoted M1 macrophage infiltration and reduced myeloid-derived suppressor cell MDSCs infiltration in HCC. The inhibition of ac4C modification induces PDL1 expression by stabilising mRNA in the myeloid cells, thereby attenuating the CTL-mediated tumour cell-killing ability. High infiltration of ac4C+CD11b+ cells is positively related to a better prognosis in patients with HCC. CONCLUSIONS: Ac4C modification of myeloid cells enhanced the HCC immunotherapy by suppressing PDL1 expression.

Resolving Ultraviolet-Visible Spectra for Complex Dissolved Mixtures of Multitudinous Organic Matters in Aerosols.

Light-absorbing organic aerosols, referred to as brown carbon (BrC), play a vital role in the global climate and air quality. Due to the complexity of BrC chromophores, the identified absorbing substances in the ambient atmosphere are very limited. However, without comprehensive knowledge of the complex absorbing compounds in BrC, our understanding of its sources, formation, and evolution mechanisms remains superficial, leading to great uncertainty in climatic and atmospheric models. To address this gap, we developed a constrained non-negative matrix factorization (NMF) model to resolve the individual ultraviolet-visible spectrum for each substance in dissolved organic aerosols, with the power of ultrahigh-performance liquid chromatography-diode array detector-ultrahigh-resolution mass spectrometry (UHPLC-DAD-UHRMS). The resolved spectra were validated by selected standard substances and validation samples. Approximately 40,000 light-absorbing substances were recognized at the MS1 level. It turns out that BrC is composed of a vast number of substances rather than a few prominent chromophores in the urban atmosphere. Previous understanding of the absorbing feature of BrC based on a few identified compounds could be biased. Weak-absorbing substances missed previously play an important role in BrC absorption when they are integrated due to their overwhelming number. This model brings the property exploration of complex dissolved organic mixtures to a molecular level, laying a foundation for identifying potentially significant compositions and obtaining a comprehensive chemical picture.

Time-varying functional connectivity predicts fluctuations in sustained attention in a serial tapping task.

The mechanisms for how large-scale brain networks contribute to sustained attention are unknown. Attention fluctuates from moment to moment, and this continuous change is consistent with dynamic changes in functional connectivity between brain networks involved in the internal and external allocation of attention. In this study, we investigated how brain network activity varied across different levels of attentional focus (i.e., "zones"). Participants performed a finger-tapping task, and guided by previous research, in-the-zone performance or state was identified by low reaction time variability and out-of-the-zone as the inverse. In-the-zone sessions tended to occur earlier in the session than out-of-the-zone blocks. This is unsurprising given the way attention fluctuates over time. Employing a novel method of time-varying functional connectivity, called the quasi-periodic pattern analysis (i.e., reliable, network-level low-frequency fluctuations), we found that the activity between the default mode network (DMN) and task positive network (TPN) is significantly more anti-correlated during in-the-zone states versus out-of-the-zone states. Furthermore, it is the frontoparietal control network (FPCN) switch that differentiates the two zone states. Activity in the dorsal attention network (DAN) and DMN were desynchronized across both zone states. During out-of-the-zone periods, FPCN synchronized with DMN, while during in-the-zone periods, FPCN switched to synchronized with DAN. In contrast, the ventral attention network (VAN) synchronized more closely with DMN during in-the-zone periods compared with out-of-the-zone periods. These findings demonstrate that time-varying functional connectivity of low frequency fluctuations across different brain networks varies with fluctuations in sustained attention or other processes that change over time.

ILGBMSH: an interpretable classification model for the shRNA target prediction with ensemble learning algorithm.

Short hairpin RNA (shRNA)-mediated gene silencing is an important technology to achieve RNA interference, in which the design of potent and reliable shRNA molecules plays a crucial role. However, efficient shRNA target selection through biological technology is expensive and time consuming. Hence, it is crucial to develop a more precise and efficient computational method to design potent and reliable shRNA molecules. In this work, we present an interpretable classification model for the shRNA target prediction using the Light Gradient Boosting Machine algorithm called ILGBMSH. Rather than utilizing only the shRNA sequence feature, we extracted 554 biological and deep learning features, which were not considered in previous shRNA prediction research. We evaluated the performance of our model compared with the state-of-the-art shRNA target prediction models. Besides, we investigated the feature explanation from the model's parameters and interpretable method called Shapley Additive Explanations, which provided us with biological insights from the model. We used independent shRNA experiment data from other resources to prove the predictive ability and robustness of our model. Finally, we used our model to design the miR30-shRNA sequences and conducted a gene knockdown experiment. The experimental result was perfectly in correspondence with our expectation with a Pearson's coefficient correlation of 0.985. In summary, the ILGBMSH model can achieve state-of-the-art shRNA prediction performance and give biological insights from the machine learning model parameters.

SULT2B1-CS-DOCK2 axis regulates effector T-cell exhaustion in HCC microenvironment.

BACKGROUND AND AIMS: HCC is a malignant disease. Compared with tyrosine kinase inhibitors (the classical therapy), immune checkpoint inhibitors are more effective in the treatment of HCC, despite their limited efficacy. Among these restricted factors, exhaustion of tumor-infiltrated lymphocytes, especially CD8 + T cells, is a core event. We aimed to determine the key factors contributing to CD8 + T-cell infiltration in HCC and investigate the underlying mechanisms. APPROACH AND RESULTS: Using machine learning and multiplex immunohistochemistry analysis, we showed that dedicator of cytokinesis protein 2 (DOCK2) was a potential indicator of infiltrated CD8 + T cells in HCC. Using RNA sequencing, flow cytometry analysis, and mouse HCC models, we demonstrated that DOCK2 inactivation accounted for infiltrated CD8 + T-cell exhaustion in tumors. Using quasi-targeted metabolomics, mass spectrum, and mass cytometry by time of flight analysis, we found that cholesterol sulfate synthesized by sulfotransferase 2B1 in tumor cells suppressed DOCK2 enzymatic activity of T cells. Through virtual screening, molecular docking simulation, and experiments validation, we demonstrated that tolazamide reversed DOCK2 inactivation-mediated CD8 + T-cell exhaustion and enhanced anti-programmed death-ligand 1 antibody+apatinib immunotherapeutic effects on HCC. CONCLUSIONS: This study indicates that DOCK2 controls CD8 + T-cell infiltration in HCC, and cholesterol sulfate synthesized by sulfotransferase 2B1 in tumor cells promotes effector T-cell exhaustion. The findings suggest that the usage of conventional drugs affects immunotherapy efficacy in HCC patients.

Systems engineering Escherichia coli for efficient production p-coumaric acid from glucose.

P-coumaric acid (p-CA), a pant metabolite with antioxidant and anti-inflammatory activity, is extensively utilized in biomedicine, food, and cosmetics industry. In this study, a synthetic pathway (PAL) for p-CA was designed, integrating three enzymes (AtPAL2, AtC4H, AtATR2) into a higher l-phenylalanine-producing strain Escherichia coli PHE05. However, the lower soluble expression and activity of AtC4H in the PAL pathway was a bottleneck for increasing p-CA titers. To overcome this limitation, the soluble expression of AtC4H was enhanced through N-terminal modifications. And an optimal mutant, AtC4HL373T/G211H, which exhibited a 4.3-fold higher kcat/Km value compared to the wild type, was developed. In addition, metabolic engineering strategies were employed to increase the intracellular NADPH pool. Overexpression of ppnk in engineered E. coli PHCA20 led to a 13.9-folds, 1.3-folds, and 29.1% in NADPH content, the NADPH/NADP+ ratio and p-CA titer, respectively. These optimizations significantly enhance p-CA production, in a 5-L fermenter using fed-batch fermentation, the p-CA titer, yield and productivity of engineered strain E. coli PHCA20 were 3.09 g/L, 20.01 mg/g glucose, and 49.05 mg/L/h, respectively. The results presented here provide a novel way to efficiently produce the plant metabolites using an industrial strain.

Ten-eleven translocation-2-mediated macrophage activation promotes liver regeneration.

BACKGROUND: The remarkable regenerative capacity of the liver enables recovery after radical Hepatocellular carcinoma (HCC) resection. After resection, macrophages secrete interleukin 6 and hepatocyte growth factors to promote liver regeneration. Ten-eleven translocation-2 (Tet2) DNA dioxygenase regulates pro-inflammatory factor secretion in macrophages. In this study, we explored the role of Tet2 in macrophages and its function independent of its enzymatic activity in liver regeneration. METHODS: The model of liver regeneration after 70% partial hepatectomy (PHx) is a classic universal model for studying reparative processes in the liver. Mice were euthanized at 0, 24, and 48 h after PHx. Enzyme-linked immunosorbent assays, quantitative reverse transcription-polymerase chain reaction, western blotting, immunofluorescence analysis, and flow cytometry were performed to explore immune cell infiltration and liver regenerative capability. Molecular dynamics simulations were performed to study the interaction between Tet2 and signal transducer and activator of transcription 1 (Stat1). RESULTS: Tet2 in macrophages negatively regulated liver regeneration in the partial hepatectomy mice model. Tet2 interacted with Stat1, inhibiting the expression of proinflammatory factors and suppressing liver regeneration. The Tet2 inhibitor attenuated the interaction between Stat1 and Tet2, enhanced Stat1 phosphorylation, and promoted hepatocyte proliferation. The proliferative function of the Tet2 inhibitor relied on macrophages and did not affect hepatocytes directly. CONCLUSION: Our findings underscore that Tet2 in macrophages negatively regulates liver regeneration by interacting with Stat1. Targeting Tet2 in macrophages promotes liver regeneration and function after a hepatectomy, presenting a novel target to promote liver regeneration and function.

LNCHC directly binds and regulates YBX1 stability to ameliorate metabolic dysfunction-associated steatotic liver disease progression.

BACKGROUND AND AIMS: Metabolic dysfunction-associated steatotic liver disease (MASLD) represents the foremost cause of chronic liver disease, yet its underlying mechanisms remain elusive. Our group previously discovered a novel long non-coding RNA (lncRNA) in rats, termed lncHC and its human counterpart, LNCHC. This study aimed to explore the role of LNCHC in the progression of MASLD. METHODS: RNA-binding proteins bound to LNCHC were searched by mass spectrometry. The target genes of LNCHC and Y-Box binding protein 1 (YBX1) were identified by RNA-seq. MASLD animal models were utilised to examine the roles of LNCHC, YBX1 and patatin-like phospholipase domain containing 3 (PNPLA3) in MASLD progression. RESULTS: Here, we identified LNCHC as a native restrainer during MASLD development. Notably, LNCHC directly binds YBX1 and prevents protein ubiquitination. Up-regulation of YBX1 then stabilises PNPLA3 mRNA to alleviate lipid accumulation in hepatocytes. Furthermore, both cell and animal studies demonstrate that LNCHC, YBX1 and PNPLA3 function to improve hepatocyte lipid accumulation and exacerbate metabolic dysfunction-associated steatohepatitis development. CONCLUSIONS: In summary, our findings unveil a novel LNCHC functionality in regulating YBX1 and PNPLA3 mRNA stability during MASLD development, providing new avenues in MASLD treatment.

A new invertebrate NPY-like polypeptide, ZoaNPY, from the Zoanthus sociatus, as a novel ligand of human NPY Y2 receptor rescues vascular insufficiency via PLC/PKC and Src- FAK-dependent signaling pathways.

Our recent multi-omics studies have revealed rich sources of novel bioactive proteins and polypeptides from marine organisms including cnidarians. In the present study, we initially conducted a transcriptomic analysis to review the composition profile of polypeptides from Zoanthus sociatus. Then, a newly discovered NPY-like polypeptide-ZoaNPY was selected for further in silico structural, binding and virtually pharmacological studies. To evaluate the pro-angiogenic effects of ZoaNPY, we employed an in vitro HUVECs model and an in vivo zebrafish model. Our results indicate that ZoaNPY, at 1-100 pmol, enhances cell survival, migration and tube formation in the endothelial cells. Besides, treatment with ZoaNPY could restore a chemically-induced vascular insufficiency in zebrafish embryos. Western blot results demonstrated the application of ZoaNPY could increase the phosphorylation of proteins related to angiogenesis signaling including PKC, PLC, FAK, Src, Akt, mTOR, MEK, and ERK1/2. Furthermore, through molecular docking and surface plasmon resonance (SPR) verification, ZoaNPY was shown to directly and physically interact with NPY Y2 receptor. In view of this, all evidence showed that the pro-angiogenic effects of ZoaNPY involve the activation of NPY Y2 receptor, thereby activating the Akt/mTOR, PLC/PKC, ERK/MEK and Src- FAK-dependent signaling pathways. Furthermore, in an excision wound model, the treatment with ZoaNPY was shown to accelerate the wound healing process in mice. Our findings provide new insights into the discovery and development of novel pro-angiogenic drugs derived from NPY-like polypeptides in the future.

Dissociation of EphB2 signaling pathways mediating progenitor cell proliferation and tumor suppression.

Signaling proteins driving the proliferation of stem and progenitor cells are often encoded by proto-oncogenes. EphB receptors represent a rare exception; they promote cell proliferation in the intestinal epithelium and function as tumor suppressors by controlling cell migration and inhibiting invasive growth. We show that cell migration and proliferation are controlled independently by the receptor EphB2. EphB2 regulated cell positioning is kinase-independent and mediated via phosphatidylinositol 3-kinase, whereas EphB2 tyrosine kinase activity regulates cell proliferation through an Abl-cyclin D1 pathway. Cyclin D1 regulation becomes uncoupled from EphB signaling during the progression from adenoma to colon carcinoma in humans, allowing continued proliferation with invasive growth. The dissociation of EphB2 signaling pathways enables the selective inhibition of the mitogenic effect without affecting the tumor suppressor function and identifies a pharmacological strategy to suppress adenoma growth.

Transferability of Machine Learning Models for Predicting Raman Spectra.

Theoretical prediction of vibrational Raman spectra enables a detailed interpretation of experimental spectra, and the advent of machine learning techniques makes it possible to predict Raman spectra while achieving a good balance between efficiency and accuracy. However, the transferability of machine learning models across different molecules remains poorly understood. This work proposed a new strategy whereby machine learning-based polarizability models were trained on similar but smaller alkane molecules to predict spectra of larger alkanes, avoiding extensive first-principles calculations on certain systems. Results showed that the developed polarizability model for alkanes with a maximum of nine carbon atoms can exhibit high accuracy in the predictions of polarizabilities and Raman spectra for the n-undecane molecule (11 carbon atoms), validating its reasonable extrapolation capability. Additionally, a descriptor space analysis method was further introduced to evaluate the transferability, demonstrating potentials for accurate and efficient Raman predictions of large molecules using limited training data labeled for smaller molecules.

Supramolecular salicylic acid combined with niacinamide in chloasma: A randomized controlled trial.

BACKGROUND: No trial of supramolecular salicylic acid (SSA) for chloasma is available yet. OBJECTIVE: The purpose of this study was to assess the efficacy and safety of Bole DA 30% supramolecular salicylic acid (SSA) combined with 10% niacinamide in treating chloasma. METHODS: This multicenter (n=15), randomized, double-blind, parallel placebo-controlled trial randomized the subjects (1:1) to Bole DA 30% SSA or placebo. The primary endpoint was the effective rate after 16 weeks using the modified melasma area severity index (mMASI) [(pretreatment-posttreatment)/pretreatment×100%]. RESULTS: This study randomized 300 subjects (150/group in the full analysis set, 144 and 147 in the per-protocol set). The total mMASI score, overall Griffiths 10 score, left Griffiths 10 score, and right Griffiths 10 score were significantly lower in the Bole DA 30% SSA group than in the placebo group (all P<0.001). One study drug-related AE and one study drug-unrelated adverse events (AE) were reported in the Bole DA 30% SSA group. No AE was reported in the placebo group. CONCLUSION: Bole DA 30% SSA combined with 10% niacinamide is effective and safe for treating chloasma. CLINICAL TRIAL REGISTRATION NUMBER: ChiCTR2200065346.

Predicting the evolution of clinical skin aging in a multi-ethnic population: Developing causal Bayesian networks using dermatological expertise.

INTRODUCTION: Software to predict the impact of aging on physical appearance is increasingly popular. But it does not consider the complex interplay of factors that contribute to skin aging. OBJECTIVES: To predict the +15-year progression of clinical signs of skin aging by developing Causal Bayesian Belief Networks (CBBNs) using expert knowledge from dermatologists. MATERIAL AND METHODS: Structures and conditional probability distributions were elicited worldwide from dermatologists with experience of at least 15 years in aesthetics. CBBN models were built for all phototypes and for ages ranging from 18 to 65 years, focusing on wrinkles, pigmentary heterogeneity and facial ptosis. Models were also evaluated by a group of independent dermatologists ensuring the quality of prediction of the cumulative effects of extrinsic and intrinsic skin aging factors, especially the distribution of scores for clinical signs 15 years after the initial assessment. RESULTS: For easiness, only models on African skins are presented in this paper. The forehead wrinkle evolution model has been detailed. Specific atlas and extrinsic factors of facial aging were used for this skin type. But the prediction method has been validated for all phototypes, and for all clinical signs of facial aging. CONCLUSION: This method proposes a skin aging model that predicts the aging process for each clinical sign, considering endogenous and exogenous factors. It simulates aging curves according to lifestyle. It can be used as a preventive tool and could be coupled with a generative AI algorithm to visualize aging and, potentially, other skin conditions, using appropriate images.