SMARCA4-deficient non small cell lung cancer (SMARCA4-dNSCLC) has recently garnered increasing attention due to its high malignancy and poor prognosis. The literature suggests that in non small cell lung cancer (NSCLC), the loss of SMARCA4 frequently co-occurs with mutations in KRAS, KEAP1, and STK11 rather than in EGFR, ALK, and ROS1. Herein, we present the first documented case of SMARCA4-dNSCLC accompanied with rare mutations of EGFR exon 20 S768I and exon 18 G719X. The patient achieved partial response with afatinib for 17 months. Our case highlights the importance of EGFR mutations in the precision targeted treatment of SMARCA4-dNSCLC.
Humans ; Afatinib/therapeutic use* ; Antineoplastic Agents/therapeutic use* ; Carcinoma, Non-Small-Cell Lung/pathology* ; DNA Helicases/genetics* ; ErbB Receptors/genetics* ; Lung Neoplasms/pathology* ; Mutation ; Nuclear Proteins/genetics* ; Transcription Factors/genetics*

Isosteviol is a tetracyclic diterpenoid compound obtained by hydrolysis of natural stevia glycoside under acidic conditions. It has many pharmacological activities, such as anti-tumor, hypoglycemic, anti-inflammatory and antibacterial. Due to its low water solubility, low activity and low bioavailability, isosteviol has poor performance. In order to overcome these shortcomings, scholars have obtained a large number of isosteviol derivatives with novel structures and excellent activity. In this paper, we review the recent progress in the research on the structure modification, biological activity, structure-activity relationship and microbial transformation of isosteviol, in order to provide a reference for the development of new drugs of isosteviol and its derivatives.

Eight compounds were isolated and purified from the ethyl acetate part of 70% acetone extract of Rehmannia glutinosa by various chromatographic techniques such as silica gel, MCI gel CHP-20, ODS, Toyopearl HW-40C, combined with TLC and semi-preparative HPLC. Their structures were elucidated by modern spectroscopy techniques (NMR, MS, UV, IR), and identified as neomartynoside A (1), osmanthuside B₆ (2), martynoside (3), isomartynoside (4), (E)-p-hydroxycinnamic acid (5), caffeic acid (6), ferulic acid (7), and methyl caffeate (8). Compound 1 is a new phenylethanol glycoside, which was identified as neomartynoside A. Compound 2 was isolated from Rehmannia glutinosa for the first time. In addition, compounds 2, 6 and 7 significantly increased relative glucose consumption, showing potential hypoglycemic activity.

OBJECTIVES: To investigate the molecular epidemiology of children with phenylketonuria (PKU) in Gansu, China, providing foundational data for intervention strategies. METHODS: A retrospective analysis was conducted on 1 159 PKU families who attended Gansu Provincial Maternity and Child Care Hospital from January 2012 to December 2024. Sanger sequencing, multiplex ligation-dependent probe amplification, whole exome sequencing, and deep intronic variant analysis were used to analyze the PAH gene. RESULTS: For the 1 159 children with PKU, 2 295 variants were identified in 2 318 alleles, resulting in a detection rate of 99.01%. The detection rates were 100% (914/914) in 457 classic PKU families, 99.45% (907/912) in 456 mild PKU families, and 96.34% (474/492) in 246 mild hyperphenylalaninemia families. The 2 295 variants detected comprised 208 distinct mutation types, among which c.728G>A (14.95%, 343/2 295) had the highest frequency, followed by c.611A>G (4.88%, 112/2 295) and c.721C>T (4.79%, 110/2 295). The cumulative frequency of the top 23 hotspot variants reached 70.28% (1 613/2 295), and most variant alleles were detected in exon 7 (29.19%, 670/2 295). CONCLUSIONS Deep intronic variant analysis of the PAH gene can improve the genetic diagnostic rate of PKU. The development of targeted detection kits for PAH hotspot variants may enable precision screening programs and enhance preventive strategies for PKU.
Humans ; Phenylketonurias/epidemiology* ; Female ; Male ; Retrospective Studies ; Phenylalanine Hydroxylase/genetics* ; Mutation ; Child, Preschool ; China/epidemiology* ; Child ; Infant

Despite therapy with potent antiviral agents, chronic hepatitis B (CHB) patients remain at high risk of hepatocellular carcinoma (HCC). While metabolites have been rediscovered as active drivers of biological processes including carcinogenesis, the specific metabolites modulating HCC risk in CHB patients are largely unknown. Here, we demonstrate that baseline plasma from CHB patients who later developed HCC during follow-up exhibits growth-promoting properties in a case-control design nested within a large-scale, prospective cohort. Metabolomics analysis reveals a reduction in long-chain acylcarnitines (LCACs) in the baseline plasma of patients with HCC development. LCACs preferentially inhibit the proliferation of HCC cells in vitro at a physiological concentration and prevent the occurrence of HCC in vivo without hepatorenal toxicity. Uptake and metabolism of circulating LCACs increase the intracellular level of acetyl coenzyme A, which upregulates histone H3 Lys14 acetylation at the promoter region of KLF6 gene and thereby activates KLF6/p21 pathway. Indeed, blocking LCAC metabolism attenuates the difference in KLF6/p21 expression induced by baseline plasma of HCC/non-HCC patients. The deficiency of circulating LCACs represents a driver of HCC in CHB patients with viral control. These insights provide a promising direction for developing therapeutic strategies to reduce HCC risk further in the antiviral era.

Objective:To develop a deep learning-based segmentation model for anterior segment ultrasound biomicroscopy (UBM) images to automatically segment the anterior segment tissues of patients with primary angle-closure glaucoma (PACG).Methods:A single-center retrospective case series was conducted.A small-scale dataset comprised 468 UBM images of the anterior chamber angle closure from 156 patients with PACG who underwent the UBM examination at Tianjin Medical University Eye Hospital between July 12, 2022, and February 20, 2023.The UBM images were randomly split into a training dataset of 228 images and a testing dataset of 152 images using a random seed method in a ratio of 6∶4.The models were trained using the PSPNet model with MobileNet V2 and ResNet50 as backbones, the DeepLab v3+ model with MobileNet V2 and Xception as backbones, and the SegFormer model with MiT-B0 and MiT-B2 as backbones.The testing dataset was used for result prediction and to achieve segmentation of four regions: the cornea and sclera, iris, ciliary body, and anterior lens surface.To evaluate the performance of the models in segmenting the anterior segment structures, multiple metrics were assessed, including the mean intersection over union (mIoU), Dice coefficient, precision, recall, false negative rate, and specificity.A comparative analysis of the test results across the different models was subsequently performed.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Tianjin Medical University Eye Hospital (No.2023KY-05).Results:The two models with the best segmentation performance were PSPNet and DeepLab v3+ .The PSPNet model with ResNet50 as the backbone achieved the mIoU of 85.11%, Dice coefficient of 91.38%, precision of 91.83%, recall of 90.94%, false negative rate of 9.06%, and specificity of 98.89%.The DeepLab v3+ model with MobileNet V2 as the backbone achieved an mIoU of 85.84%, Dice coefficient of 92.01%, precision of 92.67%, recall of 91.36%, false negative rate of 8.64%, and specificity of 98.90%.Among the five key metrics, mIoU, Dice coefficient, recall, false negative rate, and specificity, DeepLab v3+ exhibited the best segmentation performance.In addition, the DeepLab v3+ model with Xception as the backbone had the highest precision among all models, reaching 92.77%.Conclusions:The deep learning-based DeepLab v3+ model achieves precise segmentation of anterior segment tissue structures in PACG anterior segment UBM image segmentation, providing auxiliary support for clinical diagnosis.

Anxiety is a major emotional disorder manifested in the individual's expectation of future threats.The incidence rate of anxiety is about 7.3％,with the highest lifetime prevalence rate among mental health conditions.The mechanism of anxiety overlaps with depression,and anxiety is a typical symptom of various mental diseases or emotional disorders in traditional Chinese medicine.The high rates of comorbidity and disability pose serious threats to people's health.Animal models are important tools for studying anxiety and are of great use for deciphering the pathogenesis of anxiety and for developing drugs.The traditional paradigm of stress-induced anxiety,however,is relatively limited.Based on traditional theory combined with clinical and animal experimental data,we propose a new hypothesis of"insufficiency of ZhiYi'causing anxiety,defined as"an anxiety state induced by the inability of an individual to meet their own needs,limited or lacking after multiple attempts,rendered hindered and powerless by an inability to meet their desires".This hypothesis is more in line with the typical manifestations of despair,lack of pleasure,and social withdrawal in clinical patients,and is supported by traditional theory and experimental data showing"hunger but unable to eat,food but unable to obtain,and gain but not full".Based on this,the established modeling paradigm is easy to apply,with good repeatability and low cost,and can be used to establish anxiety models in rats and mice,to provide a theoretical and model basis for the development and pharmacological evaluation of anti-anxiety drugs.

Objective:To develop a deep learning-based segmentation model for anterior segment ultrasound biomicroscopy (UBM) images to automatically segment the anterior segment tissues of patients with primary angle-closure glaucoma (PACG).Methods:A single-center retrospective case series was conducted.A small-scale dataset comprised 468 UBM images of the anterior chamber angle closure from 156 patients with PACG who underwent the UBM examination at Tianjin Medical University Eye Hospital between July 12, 2022, and February 20, 2023.The UBM images were randomly split into a training dataset of 228 images and a testing dataset of 152 images using a random seed method in a ratio of 6∶4.The models were trained using the PSPNet model with MobileNet V2 and ResNet50 as backbones, the DeepLab v3+ model with MobileNet V2 and Xception as backbones, and the SegFormer model with MiT-B0 and MiT-B2 as backbones.The testing dataset was used for result prediction and to achieve segmentation of four regions: the cornea and sclera, iris, ciliary body, and anterior lens surface.To evaluate the performance of the models in segmenting the anterior segment structures, multiple metrics were assessed, including the mean intersection over union (mIoU), Dice coefficient, precision, recall, false negative rate, and specificity.A comparative analysis of the test results across the different models was subsequently performed.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Tianjin Medical University Eye Hospital (No.2023KY-05).Results:The two models with the best segmentation performance were PSPNet and DeepLab v3+ .The PSPNet model with ResNet50 as the backbone achieved the mIoU of 85.11%, Dice coefficient of 91.38%, precision of 91.83%, recall of 90.94%, false negative rate of 9.06%, and specificity of 98.89%.The DeepLab v3+ model with MobileNet V2 as the backbone achieved an mIoU of 85.84%, Dice coefficient of 92.01%, precision of 92.67%, recall of 91.36%, false negative rate of 8.64%, and specificity of 98.90%.Among the five key metrics, mIoU, Dice coefficient, recall, false negative rate, and specificity, DeepLab v3+ exhibited the best segmentation performance.In addition, the DeepLab v3+ model with Xception as the backbone had the highest precision among all models, reaching 92.77%.Conclusions:The deep learning-based DeepLab v3+ model achieves precise segmentation of anterior segment tissue structures in PACG anterior segment UBM image segmentation, providing auxiliary support for clinical diagnosis.

Anxiety is a major emotional disorder manifested in the individual's expectation of future threats.The incidence rate of anxiety is about 7.3％,with the highest lifetime prevalence rate among mental health conditions.The mechanism of anxiety overlaps with depression,and anxiety is a typical symptom of various mental diseases or emotional disorders in traditional Chinese medicine.The high rates of comorbidity and disability pose serious threats to people's health.Animal models are important tools for studying anxiety and are of great use for deciphering the pathogenesis of anxiety and for developing drugs.The traditional paradigm of stress-induced anxiety,however,is relatively limited.Based on traditional theory combined with clinical and animal experimental data,we propose a new hypothesis of"insufficiency of ZhiYi'causing anxiety,defined as"an anxiety state induced by the inability of an individual to meet their own needs,limited or lacking after multiple attempts,rendered hindered and powerless by an inability to meet their desires".This hypothesis is more in line with the typical manifestations of despair,lack of pleasure,and social withdrawal in clinical patients,and is supported by traditional theory and experimental data showing"hunger but unable to eat,food but unable to obtain,and gain but not full".Based on this,the established modeling paradigm is easy to apply,with good repeatability and low cost,and can be used to establish anxiety models in rats and mice,to provide a theoretical and model basis for the development and pharmacological evaluation of anti-anxiety drugs.

Macroporous adsorption resin, MCI, Toyopearl HW-40C and silica gel column chromatography combined with the semi-preparative HPLC were used to isolate and purify the water extract of Cornus officinalis. And the structures of compounds were identified by HRESIMS, NMR, IR, UV and ECD. A total of twelve compounds were isolated from the fruits of Cornus officinalis. They were identified as neolignan B (1), 2,3-dihydro-7-methoxy-2-(4′-hydroxy-3′-methoxyphenyl)-3a-O-β-D-xylopyranosyloxymethyl-5-benzofuranpropanol (2), cornucadinoside A (3), 3,4-dihydroxyacetophenone (4), n-butyl gallate (5), 3-hydroxybenzoic acid (6), n-butyl quininate (7), 5-hydroxymaltol (8), 2-furolic acid (9), 5-hydroxymethylfurfural (10), 5-(methoxycarbonyl)-2-pyrrolidone (11), and dimethyl malate (12). Compound 1 is a new biphenyl lignan, named as neolignan B. Compounds 2, 4, 5, 7-9 and 11 were isolated from Cornus officinalis for the first time.