G-quadruplex in the TMV Genome Regulates Viral Proliferation and Acts as Antiviral Target of Photodynamic Therapy.

Plant viruses seriously disrupt crop growth and development, and classic protein-targeted antiviral drugs could not provide complete protection against them. It is urgent to develop antiviral compounds with novel targets. Photodynamic therapy shows potential in controlling agricultural pests, but nonselective damage from reactive oxygen species (ROS) unexpectedly affects healthy tissues. A G-quadruplex (G4)-forming sequence in the tobacco mosaic virus (TMV) genome was identified to interfere the RNA replication in vitro, and affect the proliferation of TMV in tobacco. N-methyl mesoporphyrin IX stabilizing the G4 structure exhibited inhibition against viral proliferation, which was comparable to the inhibition effect of ribavirin. This indicated that G4 could work as an antiviral target. The large conjugate planes shared by G4 ligands and photosensitizers (PSs) remind us that the PSs could work as antiviral agents by targeting G4 in the genome of TMV. Chlorin e6 (Ce6) was identified to stabilize the G4 structure in the dark and selectively cleave the G4 sequence by producing ROS upon LED-light irradiation, leading to 92.2% inhibition against TMV in vivo, which is higher than that of commercial ningnanmycin. The inhibition of Ce6 was lost against the mutant variants lacking the G4-forming sequence. These findings indicated that the G-quadruplex in the TMV genome worked as an important structural element regulating viral proliferation, and could act as the antiviral target of photodynamic therapy.

Interpretable deep learning methods for multiview learning.

BACKGROUND: Technological advances have enabled the generation of unique and complementary types of data or views (e.g. genomics, proteomics, metabolomics) and opened up a new era in multiview learning research with the potential to lead to new biomedical discoveries. RESULTS: We propose iDeepViewLearn (Interpretable Deep Learning Method for Multiview Learning) to learn nonlinear relationships in data from multiple views while achieving feature selection. iDeepViewLearn combines deep learning flexibility with the statistical benefits of data and knowledge-driven feature selection, giving interpretable results. Deep neural networks are used to learn view-independent low-dimensional embedding through an optimization problem that minimizes the difference between observed and reconstructed data, while imposing a regularization penalty on the reconstructed data. The normalized Laplacian of a graph is used to model bilateral relationships between variables in each view, therefore, encouraging selection of related variables. iDeepViewLearn is tested on simulated and three real-world data for classification, clustering, and reconstruction tasks. For the classification tasks, iDeepViewLearn had competitive classification results with state-of-the-art methods in various settings. For the clustering task, we detected molecular clusters that differed in their 10-year survival rates for breast cancer. For the reconstruction task, we were able to reconstruct handwritten images using a few pixels while achieving competitive classification accuracy. The results of our real data application and simulations with small to moderate sample sizes suggest that iDeepViewLearn may be a useful method for small-sample-size problems compared to other deep learning methods for multiview learning. CONCLUSION: iDeepViewLearn is an innovative deep learning model capable of capturing nonlinear relationships between data from multiple views while achieving feature selection. It is fully open source and is freely available at https://github.com/lasandrall/iDeepViewLearn .

The recent advance and prospect of poly(ADP-ribose) polymerase inhibitors for the treatment of cancer.

Chemotherapies are commonly used in cancer therapy, their applications are limited to low specificity, severe adverse reactions, and long-term medication-induced drug resistance. Poly(ADP-ribose) polymerase (PARP) inhibitors are a novel class of antitumor drugs developed to solve these intractable problems based on the mechanism of DNA damage repair, which have been widely applied in the treatment of ovarian cancer, breast cancer, and other cancers through inducing synthetic lethal effect and trapping PARP-DNA complex in BRCA gene mutated cancer cells. In recent years, PARP inhibitors have been widely used in combination with various first-line chemotherapy drugs, targeted drugs and immune checkpoint inhibitors to expand the scope of clinical application. However, the intricate mechanisms underlying the drug resistance to PARP inhibitors, including the restoration of homologous recombination, stabilization of DNA replication forks, overexpression of drug efflux protein, and epigenetic modifications pose great challenges and desirability in the development of novel PARP inhibitors. In this review, we will focus on the mechanism, structure-activity relationship, and multidrug resistance associated with the representative PARP inhibitors. Furthermore, we aim to provide insights into the development prospects and emerging trends to offer guidance for the clinical application and inspiration for the development of novel PARP inhibitors and degraders.

Automated Quantification of Eye Tics Using Computer Vision and Deep Learning Techniques.

BACKGROUND: Tourette syndrome (TS) tics are typically quantified using "paper and pencil" rating scales that are susceptible to factors that adversely impact validity. Video-based methods to more objectively quantify tics have been developed but are challenged by reliance on human raters and procedures that are resource intensive. Computer vision approaches that automate detection of atypical movements may be useful to apply to tic quantification. OBJECTIVE: The current proof-of-concept study applied a computer vision approach to train a supervised deep learning algorithm to detect eye tics in video, the most common tic type in patients with TS. METHODS: Videos (N = 54) of 11 adolescent patients with TS were rigorously coded by trained human raters to identify 1.5-second clips depicting "eye tic events" (N = 1775) and "non-tic events" (N = 3680). Clips were encoded into three-dimensional facial landmarks. Supervised deep learning was applied to processed data using random split and disjoint split regimens to simulate model validity under different conditions. RESULTS: Area under receiver operating characteristic curve was 0.89 for the random split regimen, indicating high accuracy in the algorithm's ability to properly classify eye tic vs. non-eye tic movements. Area under receiver operating characteristic curve was 0.74 for the disjoint split regimen, suggesting that algorithm generalizability is more limited when trained on a small patient sample. CONCLUSIONS: The algorithm was successful in detecting eye tics in unseen validation sets. Automated tic detection from video is a promising approach for tic quantification that may have future utility in TS screening, diagnostics, and treatment outcome measurement. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Prediction model for pheochromocytoma/paraganglioma using nCounter assay.

BACKGROUND: World Health Organization defined pheochromocytomas/paragangliomas (PPGL) as malignant tumors in 2017 because the existing classification system could not reflect locally aggressive behavior sufficiently. However, predicting the likelihood of metastasis remains a crucial part of the treatment strategy. METHODS: From one tertiary care hospital and one secondary hospital, 97 PPGL cases were selected. Medical records of PPGL cases with the presence of formalin-fixed and paraffin-embedded (FFPE) tissue of primary lesion were reviewed. For FFPE tissues, a nCounter assay was conducted to determine differently expressed genes between metastatic and non-metastatic PPGL groups. Performances of prediction models for the likelihood of metastasis were calculated. RESULTS: Of a total of 97 PPGL cases, 39, 20, and 38 were classified as benign, malignant, and validation, respectively. In the nCounter assay, CDK1, TYMS, and TOP2A genes showed significant differences in expression. Tumor size was positively correlated with CDK1 expression level. The Lasso regression model showed supreme performance of sensitivity 91.7% and specificity 95.5% when those significant factors were considered. CONCLUSION: Machine learning of multi-modal classifiers can be used to create a prediction model for metastasis of PPGL with high sensitivity and specificity using nCounter assay. Moreover, CDK1 inhibitors could be considered for developing drug treatment.

Exploring epigenetic strategies for the treatment of osteoporosis.

The worldwide trend toward an aging population has resulted in a higher incidence of chronic conditions, such as osteoporosis. Osteoporosis, a prevalent skeletal disorder characterized by decreased bone mass and increased fracture risk, encompasses primary and secondary forms, each with distinct etiologies. Mechanistically, osteoporosis involves an imbalance between bone resorption by osteoclasts and bone formation by osteoblasts. Current pharmacological interventions for osteoporosis, such as bisphosphonates, denosumab, and teriparatide, aim to modulate bone turnover and preserve bone density. Hormone replacement therapy and lifestyle modifications are also recommended to manage the condition. While current medications offer therapeutic options, they are not devoid of limitations. Recent studies have highlighted the importance of epigenetic mechanisms, including DNA methylation and histone modifications, in regulating gene expression during bone remodeling. The use of epigenetic drugs, or epidrugs, to target these mechanisms offers a promising avenue for therapeutic intervention in osteoporosis. In this review, we comprehensively examine the recent advancements in the application of epidrugs for treating osteoporosis.

An inferior vena cava-priority approach in laparoscopic isolated hepatic caudate lobectomy.

PURPOSE: Laparoscopic isolated caudate lobectomy is still a challenging operation for surgeons. The access route of the operation plays a vital role during laparoscopic caudate lobectomy. There are few references regarding this technique. Here, we introduce a preferred inferior vena cava (IVC) approach in laparoscopic caudate lobectomy. METHODS: Twenty-one consecutive patients with caudate hepatic tumours between June 2016 and December 2021 were included in this study. All of them received laparoscopic caudate lobectomy involving an IVC priority approach. The IVC priority approach refers to prioritizing the dissection of the IVC from the liver parenchyma before proceeding with the conventional left or right approach. It emphasizes the importance of the IVC dissection during process. Clinical data, intraoperative parameters and postoperative results were evaluated. Sixteen patients were performed pure IVC priority approach, while 5 patients underwent a combined approach. We subsequently compared the intraoperative and postoperative between the two groups. RESULTS: All 21 patients were treated with laparoscopic technology. The operative time was 190.95 ± 92.65 min. The average estimated blood loss was 251.43 ± 247.45 ml, and four patients needed blood transfusions during the perioperative period. The average duration of hospital stay was 8.43 ± 2.64 (range from 6.0 to 16.0) days. Patients who underwent the pure inferior vena cava (IVC) approach required a shorter hepatic pedicle clamping time (26 vs. 55 min, respectively; P < 0.001) and operation time (150 vs. 380 min, respectively; P = 0.002) than those who underwent the combined approach. Hospitalization (7.0 vs. 9.0 days, respectively; P = 0.006) was shorter in the pure IVC group than in the combined group. CONCLUSIONS: Laparoscopic caudate lobectomy with an IVC priority approach is safe and feasible for patients with caudate hepatic tumours.

End-tidal carbon dioxide after sodium bicarbonate infusion during mechanical ventilation or ongoing cardiopulmonary resuscitation.

PURPOSE: End-tidal CO2 is used to monitor the ventilation status or hemodynamic efficacy during mechanical ventilation or cardiopulmonary resuscitation (CPR), and it may be affected by various factors including sodium bicarbonate administration. This study investigated changes in end-tidal CO2 after sodium bicarbonate administration. MATERIALS AND METHODS: This single-center, prospective observational study included adult patients who received sodium bicarbonate during mechanical ventilation or CPR. End-tidal CO2 elevation was defined as an increase of ≥20% from the baseline end-tidal CO2 value. The time to initial increase (lag time, Tlag), time to peak (Tpeak), and duration of the end-tidal CO2 rise (Tduration) were compared between the patients with spontaneous circulation (SC group) and those with ongoing resuscitation (CPR group). RESULTS: Thirty-three patients, (SC group, n = 25; CPR group, n = 8), were included. Compared with the baseline value, the median values of peak end-tidal CO2 after sodium bicarbonate injection increased by 100% (from 21 to 41 mmHg) in all patients, 89.5% (from 21 to 39 mmHg) in the SC group, and 160.2% (from 15 to 41 mmHg) in the CPR group. The median Tlag was 17 s (IQR: 12-21) and the median Tpeak was 35 s (IQR: 27-52). The median Tduration was 420 s (IQR: 90-639). The median Tlag, Tpeak, and Tduration were not significantly different between the groups. Tduration was associated with the amount of sodium bicarbonate for SC group (correlation coefficient: 0.531, p = 0.006). CONCLUSION: The administration of sodium bicarbonate may lead to a substantial increase in end-tidal CO2 for several minutes in patients with spontaneous circulation and in patients with ongoing CPR. After intravenous administration of sodium bicarbonate, the use of end-tidal CO2 pressure as a physiological indicator may be limited.

S100 Calcium-Binding Protein A9, a Potential Novel Diagnostic Biomarker for Idiopathic Pulmonary Fibrosis.

BACKGROUND: Neutrophilic inflammation is a characteristic feature of idiopathic pulmonary fibrosis (IPF). S100 calcium-binding protein A9 (S100A9) is a neutrophil-derived protein involved in the development of neutrophil-related chronic inflammatory disorders. However, the role of S100A9 in IPF remains unclear. METHODS: We used enzyme-linked immunosorbent assays to measure S100A9 levels in bronchoalveolar lavage fluid (BALF) and serum obtained from healthy controls (HCs) and patients with IPF, non-specific interstitial pneumonia, hypersensitivity pneumonitis, and sarcoidosis. RESULTS: Compared with HCs, BALF S100A9 levels were significantly higher in IPF patients (P < 0.001), patients with hypersensitivity pneumonitis (P = 0.043), and patients with nonspecific interstitial pneumonia (P < 0.001). The S100A9 level in BALF of 0.093 ng/mL could distinguish IPF patients from HCs, with a specificity of 78.8% and a sensitivity of 81.6%. Similarly, the S100A9 level in BALF of 0.239 ng/mL had a specificity of 64.7% and a sensitivity of 66.7% for distinguishing IPF patients from patients with other interstitial lung diseases. Additionally, BALF S100A9 levels were significantly correlated with neutrophil counts (r = 0.356, P < 0.001) in BALF. IPF patients with S100A9 levels in BALF > 0.533 ng/mL had lower survival rates, compared with patients who had levels ≤ 0.553 ng/mL (n = 49; hazard ratio [HR], 3.62; P = 0.021). Combination analysis revealed that IPF patients with S100A9 levels in BALF> 0.553 ng/mL or neutrophil percentages > 49.1% (n = 43) had significantly lower survival rates than patients with S100A9 levels in BALF ≤ 0.553 ng/mL and neutrophil percentages ≤ 49.1% (n = 41) (HR, 3.91; P = 0.014). Additionally, patients with serum S100A9 levels > 0.077 ng/mL (n = 29) had significantly lower survival rates than patients with levels ≤ 0.077 ng/mL (n = 53, HR, 2.52; P = 0.013). S100A9 was expressed on neutrophils and macrophages in BALF from IPF patients as well as α-smooth muscle actin positive cells in the lung tissues. CONCLUSION: S100A9 is involved in the development and progression of IPF. Moreover, S100A9 levels in BALF and serum may be surrogate markers for IPF diagnosis and survival prediction, particularly when analyzed in combination with neutrophil percentages.

Post-acute COVID-19 syndrome in previously hospitalized patients.

INTRODUCTION: With the prolongation of the COVID-19 pandemic, more individuals are experiencing sequelae after COVID-19 infection, also known as post-acute COVID-19 syndrome (PCS). The aims of this study were to describe the prevalence and characteristics of PCS symptoms such as fatigue, anxiety, and depression and to compare these symptoms according to participant characteristics in patients who had been previously hospitalized due to COVID-19. DESIGN: A descriptive cross-sectional study design was used. METHODS: We included 114 individuals who had been hospitalized for COVID-19 and were discharged from the hospital at least 4 weeks before. Symptoms were assessed using the Fatigue Severity Scale, the Hospital Anxiety-Depression Scale, and the PCS symptom questionnaire developed by the authors. We used descriptive statistics, the Student's t-test, the Wilcoxon rank-sum test, and the Kruskal-Wallis test for statistical analyses. RESULTS: The most prevalent symptoms were anxiety (66.7%), fatigue (64.0%), headache (57.9%), and concentration or memory difficulties (57.9%). Concentration or memory difficulties and sleep disturbances had the highest mean frequency. Concentration or memory difficulties were rated with the highest mean severity, and cough, loss of taste, and muscle and joint pain had the highest mean distress scores. Female participants, individuals hospitalized for more than 2 weeks, individuals discharged more than 9 months ago, unvaccinated patients, and those who tried at least one symptom relief method reported higher symptom distress. CONCLUSION: The findings of this investigation into the frequency, severity, and distress of symptoms shed light on the identification of post-COVID symptoms in detail. To objectively evaluate and comprehend the symptom trajectories of PCS, prospective studies about the development of symptom assessment tools and studies with a longitudinal design should be conducted. CLINICAL RELEVANCE: A substantial number of respondents reported numerous symptoms and expressed symptom distress; therefore, the development of nursing interventions and treatments to alleviate PCS symptoms is crucial.

Unraveling the multiple chronic conditions patterns among people with Alzheimer's disease and related dementia: A machine learning approach to incorporate synergistic interactions.

INTRODUCTION: Most people with Alzheimer's disease and related dementia (ADRD) also suffer from two or more chronic conditions, known as multiple chronic conditions (MCC). While many studies have investigated the MCC patterns, few studies have considered the synergistic interactions with other factors (called the syndemic factors) specifically for people with ADRD. METHODS: We included 40,290 visits and identified 18 MCC from the National Alzheimer's Coordinating Center. Then, we utilized a multi-label XGBoost model to predict developing MCC based on existing MCC patterns and individualized syndemic factors. RESULTS: Our model achieved an overall arithmetic mean of 0.710 AUROC (SD = 0.100) in predicting 18 developing MCC. While existing MCC patterns have enough predictive power, syndemic factors related to dementia, social behaviors, mental and physical health can improve model performance further. DISCUSSION: Our study demonstrated that the MCC patterns among people with ADRD can be learned using a machine-learning approach with syndemic framework adjustments. HIGHLIGHTS: Machine learning models can learn the MCC patterns for people with ADRD. The learned MCC patterns should be adjusted and individualized by syndemic factors. The model can predict which disease is developing based on existing MCC patterns. As a result, this model enables early specific MCC identification and prevention.

The impact of ageism experiences on social participation among community-dwelling older adults: Exploring the moderating role of digital literacy.

OBJECTIVES: This study examined how digital literacy moderates the relationship between ageism experiences and social participation among community-dwelling older adults. METHODS: Regression analysis of data from the 2020 National Survey of Older Koreans with 9,920 participants was conducted to identify the association of ageism experiences with social participation in model 1. The moderating effects of digital literacy were examined by adding an interaction term in model 2. RESULTS: In model 1, both ageism experiences and digital literacy were significant predictors of social participation. However, in model 2, the interaction term of digital literacy rendered the association between ageism experiences and social participation non-significant. Model 2 explained approximately 18.4 % of the total variance in social participation. CONCLUSIONS: By highlighting the importance of digital literacy in increasing social participation among older adults, this study offers valuable insights for interventions aimed at improving digital literacy to promote successful aging in a technology-dependent society.

The relationship between self-reported sensory decline and cognitive frailty in older persons.

This study aimed to explore the relationship between self-reported sensory decline, including poor hearing and vision, and cognitive frailty in older persons. This cross-sectional study analyzed data from the 2020 National Survey of Living Conditions and Welfare Needs of Older Koreans. Binomial logistic regression analysis was performed to analyze the relationship between self-reported sensory decline and cognitive frailty. Of 9,692 participants, 39.8 % experienced sensory decline. The prevalence of cognitive frailty was 15.7 % among participants with poor hearing, 6.9 % among those with poor vision, and 17.9 % among those with combined poor hearing and vision. In our model, adjusted for sociodemographic and health-related variables, the cognitive frailty was significantly associated with poor hearing alone and combined poor hearing and vision. The findings suggest that healthcare professionals should prioritize their attention to the risk of cognitive frailty in older adults experiencing poor hearing alone and combined with poor vision.

Associations with smartphone usage and life satisfaction among older adults: Mediating roles of depressive symptoms and cognitive function.

This study aimed to investigate the serial mediating effects of depressive symptoms and cognitive function on the relationship between smartphone usage and life satisfaction among older people. Multiple linear regression analyses for complex randomly sampled data were used to identify the association between the status/level of smartphone usage, depressive symptoms, cognitive function, and life satisfaction. Furthermore, the Sobel test was conducted to assess the serial mediating effects of depressive symptoms and cognitive function on the relationship between smartphone usage and life satisfaction. The status and level of smartphone usage had significant positive impacts on cognitive function and life satisfaction, and negative impacts on depressive symptoms. Our results suggest that nurses should assess the status and level of smartphone usage among older people and plan approaches that enable them to utilize various smartphone functions to improve the well-being of this population.

Intensive care nurses' impostor phenomenon: Associations with narcissistic personality, shame-proneness, and guilt-proneness.

BACKGROUND: Intensive care unit nurses commonly experience impostor phenomenon owing to constant exposure to urgent situations, high work pressure, and the demands of their professional roles. Impostor phenomenon may induce anxiety and self-doubt and adversely affect personal growth and career development. Therefore, identifying the severity of impostor phenomenon among intensive care unit nurses and its associated factors is important. OBJECTIVES: The aim of this study was to investigate the associations between narcissistic personality, shame-proneness, guilt-proneness, and impostor phenomenon among intensive care unit nurses in tertiary hospitals. METHODS: This cross-sectional study collected data from 251 nurses in tertiary hospitals in Korea, in July 2022, using an online questionnaire. Narcissistic personality was assessed using the Pathological Narcissism Inventory, and shame-proneness and guilt-proneness were measured using the Test of Self-Conscious Affect. Impostor phenomenon was assessed using the Clance Impostor Phenomenon Scale. RESULTS: Multiple linear regression analysis showed that the most potent factors affecting impostor phenomenon in intensive care unit nurses was shame-proneness, followed by narcissistic vulnerability and guilt-proneness. The regression model explained 65.0% of the variance. CONCLUSION: Nurse leaders can address impostor phenomenon among newly hired intensive care unit nurses with the goal of minimising adverse psychological outcomes. Shame-proneness, narcissistic vulnerability, and guilt-proneness require attention because of their relationship to impostor phenomenon. Additionally, it is necessary to plan and implement measures to promote awareness of self-conscious emotions, psychoeducation, and mental health intervention programs that focus on these factors.

A new scoring system for predicting the outcome of hepatocellular carcinoma patients without microvascular invasion-a large-scale multicentre study.

BACKGROUND: The prognosis of HCC patients without MVI (so called M0) is highly heterogeneous and the need for adjuvant therapy is still controversial. METHODS: Patients with HCC with M0 who underwent liver resection (LR) or liver transplantation (LT) as an initial therapy were included. The Eastern Hepatobiliary Surgery Hospital (EHBH)-M0 score was developed from a retrospective cohort to form the training cohort. The classification which was developed using multivariate cox regression analysis was externally validated. RESULTS: The score was developed using the following factors: α-fetoprotein level, tumour diameter, liver cirrhosis, total bilirubin, albumin and aspartate aminotransferase. The score differentiated two groups of M0 patients (≤3, >3 points) with distinct long-term prognoses outcomes (median overall survival (OS), 98.0 vs. 46.0 months; p < 0.001). The predictive accuracy of the score was greater than the other commonly used staging systems for HCC. And for M0 patients with a higher score underwent LR. Adjuvant transcatheter arterial chemoembolization (TACE) was effective to prolong OS. CONCLUSIONS: The EHBH M0 scoring system was more accurate in predicting the prognosis of HCC patients with M0 after LR or LT. Adjuvant therapy is recommended for HCC patients who have a higher score.

New drug approvals for 2022: Synthesis and clinical applications.

The U.S. Food and Drug Administration has approved a total of 37 new drugs in 2022, which are composed of 20 chemical entities and 17 biologics. In particular, 20 chemical entities, including 17 small molecule drugs, 1 radiotherapy, and 2 diagnostic agents, provide privileged scaffolds, breakthrough clinical benefits, and a new mechanism of action for the discovery of more potent clinical candidates. The structure-based drug development with clear targets and fragment-based drug development with privileged scaffolds have always been the important modules in the field of drug discovery, which could easily bypass the patent protection and bring about improved biological activity. Therefore, we summarized the relevant valuable information about clinical application, mechanism of action, and chemical synthesis of 17 newly approved small molecule drugs in 2022. We hope this timely and comprehensive review could bring about creative and elegant inspiration on the synthetic methodologies and mechanism of action for the discovery of new drugs with novel chemical scaffolds and extended clinical indications.

Novel Avian Influenza Virus (H5N1) Clade 2.3.4.4b Reassortants in Migratory Birds, China.

Two novel reassortant highly pathogenic avian influenza viruses (H5N1) clade 2.3.4.4b.2 were identified in dead migratory birds in China in November 2021. The viruses probably evolved among wild birds through different flyways connecting Europe and Asia. Their low antigenic reaction to vaccine antiserum indicates high risks to poultry and to public health.

LIN28A loss of function is associated with Parkinson's disease pathogenesis.

Parkinson's disease (PD) is neurodegenerative movement disorder characterized by degeneration of midbrain-type dopamine (mDA) neurons in the substantia nigra (SN). The RNA-binding protein Lin28 plays a role in neuronal stem cell development and neuronal differentiation. In this study, we reveal that Lin28 conditional knockout (cKO) mice show degeneration of mDA neurons in the SN, as well as PD-related behavioral deficits. We identify a loss-of-function variant of LIN28A (R192G substitution) in two early-onset PD patients. Using an isogenic human embryonic stem cell (hESC)/human induced pluripotent stem cell (hiPSC)-based disease model, we find that the Lin28 R192G variant leads to developmental defects and PD-related phenotypes in mDA neuronal cells that can be rescued by expression of wild-type Lin28A. Cell transplantation experiments in PD model rats show that correction of the LIN28A variant in the donor patient (pt)-hiPSCs leads to improved behavioral phenotypes. Our data link LIN28A to PD pathogenesis and suggest future personalized medicine targeting this variant in patients.

Dimethyl alpha-ketoglutarate inhibits proliferation in diffuse intrinsic pontine glioma by reprogramming epigenetic and transcriptional networks.

Diffuse intrinsic pontine glioma (DIPG) is a highly aggressive pediatric brain tumor with limited therapeutic options. Here, we investigated the potential of dimethyl alpha-ketoglutarate (DMKG) as an anti-proliferative agent against DIPG and unraveled its underlying molecular mechanisms. DMKG exhibited robust inhibition of DIPG cell proliferation, colony formation, and neurosphere growth. Transcriptomic analysis revealed substantial alterations in gene expression, with upregulated genes enriched in hypoxia-related pathways and downregulated genes associated with cell division and the mitotic cell cycle. Notably, DMKG induced G1/S phase cell cycle arrest and downregulated histone H3 lysine 27 acetylation (H3K27ac) without affecting H3 methylation levels. The inhibition of AKT and ERK signaling pathways by DMKG coincided with decreased expression of the CBP/p300 coactivator. Importantly, we identified the c-MYC-p300/ATF1-p300 axis as a key mediator of DMKG's effects, demonstrating reduced binding to target gene promoters and decreased H3K27ac levels. Depletion of c-MYC or ATF1 effectively inhibited DIPG cell growth. These findings highlight the potent anti-proliferative properties of DMKG, its impact on epigenetic modifications, and the involvement of the c-MYC-p300/ATF1-p300 axis in DIPG, shedding light on potential therapeutic strategies for this devastating disease.