Hepatitis B virus （HBV） infection is the primary cause of viral hepatitis and represents a substantial disease burden in China. However， effective and safe agents capable of completely eliminating HBV DNA are still lacking. In modern medicine， anti-HBV strategies mainly target covalently closed circular DNA （cccDNA）， among other mechanisms， and multiple novel drugs are currently under clinical investigation. Traditional medicine has been shown to exert anti-HBV effects through direct pathways， such as blocking viral entry， as well as indirect pathways， including the regulation of programmed cell death. Studies have confirmed that the integration of traditional Chinese medicine （TCM） and Western medicine in treating HBV infection and its related complications offers complementary advantages， particularly in enhancing HBV clearance rates， improving liver function， preventing various complications， and delaying the progression from hepatic fibrosis to hepatocellular carcinoma. This review focuses on advances in anti-HBV research involving TCM， Western medicine， and their integrated application， aiming to provide a basis for integrated HBV therapy and new drug development.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

This consensus will introduce the characteristics of fillers used in the surgical cavities of domestic nasal surgery patients based on relevant literature and expert opinions. It will also provide recommendations for the selection of cavity fillers for different nasal diseases, with chronic sinusitis as a representative example.
Humans ; Nasal Cavity/surgery* ; Nasal Surgical Procedures ; China ; Consensus ; Sinusitis/surgery* ; Dermal Fillers

The p21-activated kinase 4 (PAK4), a key regulator of malignancy, is negatively correlated with immune infiltration and has become an emergent drug target of cancer therapy. Given the lack of high efficacy PAK4 inhibitors, we herein reported the identification of a novel inhibitor 13 bearing a tetrahydrobenzofuro[2,3-c]pyridine tricyclic core and possessing high potency against MIA PaCa-2 and Pan02 cell lines with IC₅₀ values of 0.38 and 0.50 μmol/L, respectively. This compound directly binds to PAK4 in a non-ATP competitive manner. In the mouse Pan02 model, compound 13 exhibited significant tumor growth inhibition at a dose of 100 mg/kg, accompanied by reduced levels of PAK4 and its phosphorylation together with immune infiltration in mice tumor tissue. Overall, compound 13 is a novel allosteric PAK4 inhibitor with a unique tricyclic structural feature and high potency both in vitro and in vivo, thus making it worthy of further exploration.

Ischemic stroke (IS) is a globally life-threatening disease. Presently, few therapeutic medicines are available for treating IS, and rt-PA is the only drug approved by the US Food and Drug Administration (FDA) in the US. In fact, many agents showing excellent neuroprotection but no blood flow-improving activity in animals have not achieved ideal clinical efficacy, while thrombolytic drugs only improving blood flow without neuroprotection have limited their wider application. To address these challenges and meet the huge unmet clinical need, we have designed and identified a novel compound AAPB with dual effects of neuroprotection and cerebral blood flow improvement. AAPB significantly reduced cerebral infarction and neural function deficit in tMCAO rats, pMCAO rats, and IS rhesus monkeys, as well as displayed exceptional safety profiles and excellent pharmacokinetic properties in rats and dogs. AAPB has now entered phase I of clinical trials fighting IS in China.

As the brain's resident immune cells, microglia perform crucial functions such as phagocytosis, neuronal network maintenance, and injury restoration by adopting various phenotypes. Dynamic imaging of these phenotypes is essential for accessing brain diseases and therapeutic responses. Although numerous probes are available for imaging pro-inflammatory microglia, no PET tracers have been developed specifically to visualize anti-inflammatory microglia. In this study, we present an ¹⁸F-labeled PET tracer (QTFT) that targets the P2Y₁₂, a receptor highly expressed on anti-inflammatory microglia. [¹⁸F]QTFT exhibited high binding affinity to the P2Y₁₂ (14.43 nmol/L) and superior blood-brain barrier permeability compared to other candidates. Micro-PET imaging in IL-4-induced neuroinflammation models showed higher [¹⁸F]QTFT uptake in lesions compared to the contralateral normal brain tissues. Importantly, this specific uptake could be blocked by QTFT or a P2Y₁₂ antagonist. Furthermore, [¹⁸F]QTFT visualized brain lesions in mouse models of epilepsy, glioma, and aging by targeting the aberrantly expressed P2Y₁₂ in anti-inflammatory microglia. In a pilot clinical study, [¹⁸F]QTFT successfully located epileptic foci, showing enhanced radioactive signals in a patient with epilepsy. Collectively, these studies suggest that [¹⁸F]QTFT could serve as a valuable diagnostic tool for imaging various brain disorders by targeting P2Y₁₂ overexpressed in anti-inflammatory microglia.

Idiopathic pulmonary fibrosis (IPF), a chronic interstitial lung disease, is characterized by aberrant wound healing, excessive scarring and the formation of myofibroblastic foci. Although the role of alternative splicing (AS) in the pathogenesis of organ fibrosis has garnered increasing attention, its specific contribution to pulmonary fibrosis remains incompletely understood. In this study, we identified an up-regulation of serine/arginine-rich splicing factor 7 (SRSF7) in lung fibroblasts derived from IPF patients and a bleomycin (BLM)-induced mouse model, and further characterized its functional role in both human fetal lung fibroblasts and mice. We demonstrated that enhanced expression of Srsf7 in mice spontaneously induced alveolar collagen accumulation. Mechanistically, we investigated alternative splicing events and revealed that SRSF7 modulates the alternative splicing of pyruvate kinase (PKM), leading to metabolic dysregulation and fibroblast activation. In vivo studies showed that fibroblast-specific knockout of Srsf7 in conditional knockout mice conferred resistance to bleomycin-induced pulmonary fibrosis. Importantly, through drug screening, we identified lomitapide as a novel modulator of SRSF7, which effectively mitigated experimental pulmonary fibrosis. Collectively, our findings elucidate a molecular pathway by which SRSF7 drives fibroblast metabolic dysregulation and propose a potential therapeutic strategy for pulmonary fibrosis.

T-cell acute lymphoblastic leukemia (T-ALL) is a highly aggressive hematologic malignancy with a poor prognosis, despite advancements in treatment. Many patients struggle with relapse or refractory disease. Investigating the role of the super-enhancer (SE) regulated gene ubiquitin-specific protease 20 (USP20) in T-ALL could enhance targeted therapies and improve clinical outcomes. Analysis of histone H3 lysine 27 acetylation (H3K27ac) chromatin immunoprecipitation sequencing (ChIP-seq) data from six T-ALL cell lines and seven pediatric samples identified USP20 as an SE-regulated driver gene. Utilizing the Cancer Cell Line Encyclopedia (CCLE) and BloodSpot databases, it was found that USP20 is specifically highly expressed in T-ALL. Knocking down USP20 with short hairpin RNA (shRNA) increased apoptosis and inhibited proliferation in T-ALL cells. In vivo studies showed that USP20 knockdown reduced tumor growth and improved survival. The USP20 inhibitor GSK2643943A demonstrated similar anti-tumor effects. Mass spectrometry, RNA-Seq, and immunoprecipitation revealed that USP20 interacted with hypoxia-inducible factor 1 subunit alpha (HIF1A) and stabilized it by deubiquitination. Cleavage under targets and tagmentation (CUT&Tag) results indicated that USP20 co-localized with HIF1A, jointly modulating target genes in T-ALL. This study identifies USP20 as a therapeutic target in T-ALL and suggests GSK2643943A as a potential treatment strategy.