The clustered regularly interspaced short palindromic repeats (CRISPR)/associated protein 9 (CRISPR /Cas9) immune system is an adaptive immune system widely distributed in bacteria and archaea. It precisely defends against invasion by exogenous phages, viruses, and plasmids through sequence-specific endogenous immune response mechanisms. As the most prominent member of this family, the CRISPR/Cas9 system has evolved into the most widely applied, flexible, and efficient technical platform in the field of genome engineering due to its exceptional genome modification capabilities. Within the CRISPR/Cas9 system, the Cas9 protein, precisely guided by a single-stranded guide RNA (gRNA), can specifically recognize target DNA sequences and induce double-strand breaks. This activates the cell’s DNA repair mechanisms, enabling gene knockout, knock-in, or modification. Demonstrating significant advantages in specificity, flexibility, and operability, CRISPR/Cas9 technology has shown immense potential in the medical field, opening new avenues for modernizing traditional Chinese medicine (TCM) research. On one hand, this technology can be used to construct precise disease models and tailor personalized treatment plans. It enables in-depth elucidation of the molecular mechanisms underlying the action targets and signaling pathways of TCM formulas and active components, thereby unraveling the scientific secrets of their complex mechanisms of action. On the other hand, it demonstrates powerful tool value in improving TCM germplasm resources, identifying and screening superior varieties, evaluating the controllability of TCM quality, and producing innovative drugs, providing technical support for the standardization and precision of TCM. Simultaneously, the high-throughput omics data generated by CRISPR technology is driving artificial intelligence (AI) to construct virtual disease models and drug prediction systems. This empowers the intelligent screening of effective TCM components, the precise prediction of potential targets, and the exploration of “reducing toxicity while enhancing efficacy” through formula combinations. This synergistic innovation between CRISPR and AI aligns perfectly with precision medicine’s urgent demand for personalized, efficient drug development, injecting new momentum into the modernization and transformation of TCM. This paper first systematically reviews and explains the developmental trajectory, structural basis, and action mechanisms of the CRISPR/Cas9 system, tracing its scientific evolution from a bacterial immune system to a gene-editing tool. It then comprehensively outlines the current state of convergence between precision medicine concepts and modernization research in TCM, analyzing the synergistic points and potential spaces for their integration. Against the backdrop of rapid precision medicine advancement, this paper emphasizes how CRISPR/Cas9 gene editing technology empowers in-depth analysis of TCM mechanisms—including specific applications in disease model construction, therapeutic target validation, and multi-target network regulation studies. It further elaborates on its multidimensional practical contributions to modernizing TCM, spanning key domains such as germplasm resource innovation, bioactive compound biosynthesis, quality standardization control, and novel TCM drug development. Finally, this paper envisions the future landscape of deep integration between CRISPR technology and AI: from data-driven intelligent drug screening to high-throughput precision discovery of effective TCM components, and further to intelligent model construction based on “reducing toxicity while enhancing efficacy” mechanisms. The synergistic convergence of these multidimensional technologies will pioneer new scientific paradigms and translational pathways for TCM modernization, propelling TCM toward leapfrogging development in the era of precision medicine.

ObjectiveThe widespread adoption of portable fundus cameras for primary care and community screening is hindered by limitations in current autofocus(AF) technologies. Image-based methods relying on sharpness evaluation require iterative searches, resulting in slow convergence, while projection-based techniques are susceptible to optical artifacts and calibration errors. To address these challenges, this study introduces a novel AF system based on direct wavefront sensing, designed to deliver simultaneous high speed, high precision, and operational robustness within the compact form factor essential for portable ophthalmic devices. MethodsOur approach fundamentally reimagines the AF process by directly measuring the ocular wavefront aberration. We developed a custom portable fundus camera integrating a miniaturized Shack-Hartmann wavefront sensor (SHWS) into the optical path. An 850 nm laser diode projects a point source onto the retina via oblique illumination to minimize corneal reflections. Light scattered from this spot carries the eye’s refractive error through the imaging optics and is directed to the SHWS, positioned at a plane optically conjugate to the primary color CMOS imaging sensor. A microlens array within the SHWS samples the incident wavefront, generating a pattern of focal spots on a CCD. Real-time centroid analysis of these spots provides a map of local wavefront slopes. These measurements are processed through a singular value decomposition (SVD) algorithm to fit a Zernike polynomial basis set, enabling real-time reconstruction of the wavefront phase. The defocus component (S) is extracted from the second-order Zernike coefficients, providing a direct, quantitative measure of the refractive error in diopters. This value serves as a precise error signal in a closed-loop control system, which commands a voice-coil actuated focusing lens to its null position in a single, deterministic step, eliminating the need for iterative search algorithms. ResultsComprehensive evaluation demonstrated the system’s high performance. Testing on a calibrated model eye (OEMI-7) established a highly linear relationship between the computed defocus S and the focusing lens position across a ±20 Diopter (D) compensation range, achievable within a 5 mm mechanical travel. The system achieved a focusing precision of 0.08 D, corresponding to an 18-fold improvement over a conventional projection spot-size method tested under identical conditions. The total focus acquisition time, encompassing wavefront measurement, computation, and lens actuation, averaged under 0.5 s. Clinical validation with 25 human volunteers (50 eyes, refractive range -15 D to +10 D) confirmed practical efficacy. The wavefront-sensing AF succeeded in 92% of attempts with a mean time of 0.5 s, substantially outperforming a projection-based benchmark which achieved only a 32% success rate with an average time of 4.25 s. The system provided instantaneous directional guidance and maintained stability during minor ocular movements. Objective assessment of image quality, via amplitude contrast of retinal vasculature, showed consistent and significant enhancement following AF correction across the entire tested diopter range. ConclusionThis work successfully implements and validates a direct wavefront-sensing autofocus paradigm for portable fundus cameras. By directly quantifying and compensating for the optical defocus aberration, this method bypasses the fundamental limitations of image-processing and projection-based techniques, enabling rapid, precise, and deterministic diopter compensation. The developed system delivers an exceptional combination of a wide operational range (±20 D), high accuracy (0.08 D), fast convergence (0.5 s), and a compact physical footprint. This technology provides a practical and high-performance focusing solution capable of enhancing the reliability, throughput, and diagnostic utility of portable retinal imaging in large-scale screening applications. Future efforts will be directed towards system cost optimization and performance adaptation for diverse ocular conditions.

The pathogenesis of metabolic dysfunction-associated steatotic liver disease （MASLD） is fundamentally rooted in spleen deficiency and is closely associated with phlegm turbidity， damp-heat and blood stasis. Clinically， liver constraint with spleen deficiency and internal retention of damp turbidity represent the predominant traditional Chinese medicine （TCM） syndrome patterns. Researches have indicated intrinsic connections between the syndrome patterns and biological indicators such as gut microbiota and metabolic profiles. Regarding treatment， classical famous formulas， modern empirical formulas， and newly developed TCM drugs show positive effects in regulating glucose and lipid metabolism， improving insulin resistance， and alleviating metabolic inflammation， exhibiting multi-target mechanisms of action； acupuncture and other external therapies also provide adjunctive value. Nevertheless， current researches still have limitations such as the lack of high-quality clinical evidence and insufficient systematic elucidation of the uncerlying mechanisms. Future efforts should focus on conducting high-quality TCM clinical trials with hard endpoint outcomes such as hepatic histology outcomes， and utilizing modern technologies like multi-omics to elucidate TCM's mechanisms of action， thereby advancing the position of TCM as a first-line therapeutic strategy for MASLD.

Objective To analyze the effect of releasing the lower pulmonary ligament on right residual lung expansion after right upper lobe resection under different body mass index (BMI) levels. Methods The clinical data of patients who underwent thoracoscopic right upper lobe resection in the First Affiliated Hospital with Nanjing Medical University from 2021 to 2022 were retrospectively analyzed. Patients were divided into a group A (17 kg/m2＜BMI≤23 kg/m2), a group B (23 kg/m2＜BMI≤29 kg/m2) and a group C (BMI＞29 kg/m2) according to BMI. The presence of residual cavity was judged by chest X-ray at 7-10 days after operation, the degree of compensation change of the right main bronchus angle was measured, and the changes in lung volume were determined by CT three-dimensional reconstruction. Results A total of 157 patients who underwent thoracoscopic right upper lobe resection were included, including 71 males and 86 females, with an average age of (59.7±11.2) years. There were 50 patients in the group A, 75 patients in the group B, and 32 patients in the group C. In the group A, compared with those without releasing the lower pulmonary ligament, patients with releasing had a lower incidence of postoperative residual cavity (P=0.016), greater changes in bronchus angle (P<0.001), and smaller changes in lung volume (P<0.001). In the group B and C, there was no significant effect of releasing the lower pulmonary ligament on postoperative residual cavity, bronchus angle, and lung volume changes (P>0.05). Conclusion For patients with thin and long body shape and low BMI, releasing the lower pulmonary ligament is helpful to promote the expansion of the residual lung after right upper lobe resection and reduce the occurrence of postoperative residual cavity in patients.

Pulmonary fibrosis（PF） is a progressive and life-threatening disease with limited therapeutic options， highlighting the urgent need for innovative drug discovery strategies. To address this challenge， the authors propose the formula-originated rational intelligent screening&translation（FIRST）， a systematic framework for developing anti-fibrotic monomers derived from classical traditional Chinese medicine（TCM）. The strategy integrates three key dimensions， including tissue-oriented intelligent screening of active compounds， structural optimization based on drug-target spatial interactions and plant biosynthetic pathways， and cross-scale validation of drug. We further highlight its applications in discovering tissue-oriented novel drugs from clinically validated TCM， the development and mechanistic elucidation of anti-fibrotic therapeutics， as well as the clinical translation and secondary development of candidate drugs. This strategy paves the way for first-in-class， formula-derived monomeric drugs with defined structures， clarified mechanisms， and proven safety， offering a transformative avenue to meet the urgent therapeutic needs of PF and setting a new paradigm for TCM-based drug innovation.

Pulmonary fibrosis（PF） is a progressive and life-threatening disease with limited therapeutic options， highlighting the urgent need for innovative drug discovery strategies. To address this challenge， the authors propose the formula-originated rational intelligent screening&translation（FIRST）， a systematic framework for developing anti-fibrotic monomers derived from classical traditional Chinese medicine（TCM）. The strategy integrates three key dimensions， including tissue-oriented intelligent screening of active compounds， structural optimization based on drug-target spatial interactions and plant biosynthetic pathways， and cross-scale validation of drug. We further highlight its applications in discovering tissue-oriented novel drugs from clinically validated TCM， the development and mechanistic elucidation of anti-fibrotic therapeutics， as well as the clinical translation and secondary development of candidate drugs. This strategy paves the way for first-in-class， formula-derived monomeric drugs with defined structures， clarified mechanisms， and proven safety， offering a transformative avenue to meet the urgent therapeutic needs of PF and setting a new paradigm for TCM-based drug innovation.

ObjectiveIn the context of the digital transformation of education, this study aims to construct a triadic interactive teaching model for surgical animal surgery in clinical medicine using modern information technology. It explores the effectiveness of different teaching methods in improving students' practical skills, aseptic awareness, and teamwork abilities, providing a reference for the reform of clinical practice education. MethodsA quasi-experimental research design was adopted. A total of 80 students from the eight-year clinical medicine program at Nanjing University were selected, including the Class of 2020 (control group, n=40) and the Class of 2021 (experimental group, n=40). The control group received traditional teaching methods, while the experimental group implemented the "Teacher-Student-AI" triadic interactive teaching model. This model utilized a smart teaching platform for personalized pre-class preparation , as well as data-driven post-class review and feedback throughout the entire teaching process. The "assessment indicators and scoring criteria for the surgical animal surgery course" were used to evaluate teaching effectiveness, with independent samples t-tests used for statistical analysis. ResultsPre-course assessments revealed no statistically significant differences in baseline theoretical knowledge or practical skills between the two groups (P>0.05). Upon completion of the course, the experimental group achieved higher scores than the control group across three key dimensions: practical skills (47.98±1.34 vs 46.92±2.51, P=0.022), aseptic awareness (17.84±1.16 vs 16.94±2.29, P=0.029), and teamwork (16.82±1.44 vs 15.95±1.22, P=0.004). However, no statistically significant difference was observed in the scores for humane care awareness between the two groups (8.24±0.70 vs 8.16±0.53, P=0.589). ConclusionThe AI-based triadic interactive teaching model can, to some extent, address the limitations of traditional surgical animal surgery education. It plays a positive role in enhancing medical students' surgical skills, aseptic awareness, and collaborative abilities. This model facilitates the transition from traditional to personalized teaching and offers a practical framework for the digital reform of clinical practice education.

ObjectiveTo investigate the effects of Chaihu Jia Longgu Mulitang（CJLM） on depression-like behaviors and neuroinflammation in rats subjected to social isolation combined with chronic unpredictable mild stress（CUMS）， and to explore the potential underlying mechanisms. MethodsSixty male SD rats were randomly divided into a normal group， a model group， and low-， medium-， and high-dose CJLM groups（2.89， 5.78， 11.56 g·kg^-1）， as well as a fluoxetine group（10 mg·kg^-1）. Except for the normal group， all other groups were subjected to social isolation combined with CUMS for 63 d. During the first 35 d， depression models were established only， and from day 36 onward， modeling and drug administration were conducted simultaneously for a total intervention period of 28 d. Depression-like behaviors were evaluated using the sucrose preference test， open-field test， and forced swimming test. Hematoxylin-eosin（HE） staining was performed to observe hippocampal histomorphology. Immunohistochemistry（IHC） was used to detect the expression levels of ionized calcium-binding adapter molecule 1（Iba1） and gasdermin D（GSDMD） proteins in the hippocampus. Western blot analysis was employed to determine the protein expression levels of adenosine 5′-monophosphate（AMP）-activated protein kinase（AMPK） and phosphorylated（p）-AMPK， silent information regulator 1（SIRT1）， nuclear factor-κB（NF-κB） and p-NF-κB， NOD-like receptor protein 3（NLRP3）， and Caspase-1 in the hippocampus. Real-time quantitative polymerase chain reaction（Real-time PCR） was used to detect the mRNA expression levels of tumor necrosis factor-α（TNF-α）， interleukin（IL）-6， and IL-1β in the hippocampus. ResultsCompared with the normal group， the model group showed a decreased sucrose preference rate（P<0.01）， reduced total movement distance（P<0.01）， prolonged immobility time（P<0.01）， and decreased central zone residence time（P<0.01） in the open-field test， and increased immobility time in the forced swimming test（P<0.01）. Hippocampal neuronal structure was damaged. The contents of Iba1 and GSDMD in the hippocampus were significantly increased（P<0.01）. The protein expression levels of p-AMPK and SIRT1 in the hippocampus were significantly decreased（P<0.01）， whereas the protein expression levels of p-NF-κB， NLRP3， and Caspase-1 were significantly increased（P<0.01）. The mRNA expression levels of IL-1β， IL-6， and TNF-α in the hippocampus were significantly upregulated（P<0.01）. Compared with the model group， the low-， medium-， and high-dose CJLM groups and the fluoxetine group all were able to reverse depression-like behavioral changes， as evidenced by increased sucrose preference rate， increased total movement distance with shortened immobility time in the open-field test， prolonged central zone residence time， and reduced immobility time in the forced swimming test（P<0.05， P<0.01）. Meanwhile， hippocampal neuronal structural damage was alleviated. In the hippocampus， the expression levels of Iba1 and GSDMD were downregulated， the expression levels of p-AMPK and SIRT1 were upregulated， and the abnormal elevations of p-NF-κB， NLRP3， Caspase-1， as well as IL-1β， IL-6， and TNF-α mRNA were suppressed（P<0.05， P<0.01）. ConclusionCJLM can ameliorate depression-like behaviors in rats subjected to social isolation combined with CUMS and attenuate hippocampal neuroinflammation and pyroptosis， suggesting that its effects may be associated with the regulation of AMPK/SIRT1/NF-κB/NLRP3 signaling pathway.

ObjectiveThe aim of this study was to investigate the prophylactic effects of caloric restriction (CR) on lipopolysaccharide (LPS)-induced septic cardiomyopathy (SCM) and to elucidate the mechanisms underlying the cardioprotective actions of CR. This research aims to provide innovative strategies and theoretical support for the prevention of SCM. MethodsA total of forty-eight 8-week-old male C57BL/6 mice, weighing between 20-25 g, were randomly assigned to 4 distinct groups, each consisting of 12 mice. The groups were designated as follows: CON (control), LPS, CR, and CR+LPS. Prior to the initiation of the CR protocol, the CR and CR+LPS groups underwent a 2-week acclimatization period during which individual food consumption was measured. The initial week of CR intervention was set at 80% of the baseline intake, followed by a reduction to 60% for the subsequent 5 weeks. After 6-week CR intervention, all 4 groups received an intraperitoneal injection of either normal saline or LPS (10 mg/kg). Twelve hours post-injection, heart function was assessed, and subsequently, heart and blood samples were collected. Serum inflammatory markers were quantified using enzyme-linked immunosorbent assay (ELISA). The serum myocardial enzyme spectrum was analyzed using an automated biochemical instrument. Myocardial tissue sections underwent hematoxylin and eosin (HE) staining and immunofluorescence (IF) staining. Western blot analysis was used to detect the expression of protein in myocardial tissue, including inflammatory markers (TNF-α, IL-9, IL-18), oxidative stress markers (iNOS, SOD2), pro-apoptotic markers (Bax/Bcl-2 ratio, CASP3), and SIRT3/SIRT6. ResultsTwelve hours after LPS injection, there was a significant decrease in ejection fraction (EF) and fractional shortening (FS) ratios, along with a notable increase in left ventricular end-systolic diameter (LVESD). Morphological and serum indicators (AST, LDH, CK, and CK-MB) indicated that LPS injection could induce myocardial structural disorders and myocardial injury. Furthermore, 6-week CR effectively prevented the myocardial injury. LPS injection also significantly increased the circulating inflammatory levels (IL-1β, TNF-α) in mice. IF and Western blot analyses revealed that LPS injection significantly up-regulating the expression of inflammatory-related proteins (TNF-α, IL-9, IL-18), oxidative stress-related proteins (iNOS, SOD2) and apoptotic proteins (Bax/Bcl-2 ratio, CASP3) in myocardial tissue. 6-week CR intervention significantly reduced circulating inflammatory levels and downregulated the expression of inflammatory, oxidative stress-related proteins and pro-apoptotic level in myocardial tissue. Additionally, LPS injection significantly downregulated the expression of SIRT3 and SIRT6 proteins in myocardial tissue, and CR intervention could restore the expression of SIRT3 proteins. ConclusionA 6-week CR could prevent LPS-induced septic cardiomyopathy, including cardiac function decline, myocardial structural damage, inflammation, oxidative stress, and apoptosis. The mechanism may be associated with the regulation of SIRT3 expression in myocardial tissue.

ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.