Colorectal cancer （CRC） is one of the most common cancers， with its incidence ranking high among cancers. It stands as the second leading cause of cancer-related death worldwide. In the early stages， CRC lacks specific symptoms， and most patients are diagnosed at advanced stages， making it a major research focus in the field of gastrointestinal tumors. Currently， clinical CRC treatments face several common challenges， including high surgical risks， frequent metastasis and recurrence， drug resistance， and significant side effects from chemotherapy and radiation therapy. With the development and application of traditional Chinese medicine （TCM）， it has been found that TCM and its active ingredients can effectively inhibit CRC cell proliferation， invasion， migration， and angiogenesis， and promote apoptosis and autophagy， thereby slowing the progression of CRC. This has become a key focus of CRC treatment research. Salvia Miltiorrhiza has multiple pharmacological effects， including activating blood circulation to dispel blood stasis， unlocking meridians to relieve pain， clearing heat to calm irritability， and cooling blood to reduce abscesses. It contains a variety of chemical components， including diterpenoids， phenolic acids， flavonoids， polysaccharides， nitrogen-containing compounds， steroids， and lactone compounds. This review summarized the molecular mechanisms of Salvia miltiorrhiza and its active ingredients in the treatment of CRC. It is found that these ingredients exert anti-CRC effects through various molecular mechanisms， including cell cycle arrest， promotion of apoptosis， inhibition of cell invasion and migration， induction of autophagy， suppression of tumor angiogenesis， and remodeling of the tumor microenvironment. The review aims to provide new insights for the drug development and clinical application of Salvia miltiorrhiza in CRC treatment.

Background Per- and poly-fluoroalkyl substances (PFAS) can influence gestational diabetes mellitus (GDM); however, current studies on their association are limited and have yielded inconsistent findings. Objective To investigate the association between maternal exposure to PFAS, as measured by urinary concentrations in early pregnancy, and the risk of developing GDM. Methods Based on the Wuhu Birth Cohort in Anhui Province conducted between 2020 and 2023, this study included 1910 pregnant women. Urine samples were collected during the first trimester, and a 75 g oral glucose tolerance test (OGTT) was completed at 24–28 gestational weeks. The concentrations of six PFAS in urine were measured using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). Participants were categorized into low, medium, and high exposure groups according to tertiles of individual PFAS concentrations. Logistic regression analysis was employed to assess the potential impact of early-pregnancy PFAS exposure levels on the risk of GDM, with stratified analyses by maternal age and fetal sex. A generalized weighted quantile sum regression (gWQS) model was applied to evaluate the relative weight distribution of the six PFAS in their mixture effect. Results The logistic regression results revealed that both medium exposure to perfluorohexane sulfonate (PFHxS) (OR=1.475, 95%CI: 1.040, 2.093) and high exposure to perfluorononanoic acid (PFNA) (OR=1.430, 95%CI: 1.013, 2.018) were positively associated with diagnosed GDM. This association was more pronounced among women older than 28 years. Among pregnant women carrying male fetuses, high-level exposures to per fluorohexanoic acid (PFHxA) (OR=1.708, 95%CI: 1.006, 2.899) and PFHxS (OR=2.116, 95%CI: 1.247, 3.584) showed positive associations with diagnosed GDM. In contrast, among those carrying female fetuses, moderate perfluorooctanesulfonic acid (PFOS) exposure was associated with a lower risk of GDM (OR=0.542, 95%CI: 0.311, 0.946). The gWQS results demonstrated that PFOS (0.48) and perfluorobutane sulfonate (PFBS) (0.34) contributed the most to the mixture effect. Conclusion Early-pregnancy exposure to PFAS (particularly PFHxS and PFNA) may be an etiological factor for GDM, with maternal age and fetal sex potentially acting as effect modifiers in this association.

In the context of the modernization of traditional Chinese medicine （TCM）， the inheritance of the experiences of famous doctors faces significant challenges due to its complex nonlinear characteristics and dynamic evolution. There are still issues in the current inheritance system， such as the homogenization of talent cultivation models， lack of standardized mentoring practices， and monotonous evaluation method， which hinder the systematic inheritance and innovative development of famous doctors' experiences. Based on a systematic review of the current state of artificial intelligence （AI）-assisted inheritance of famous doctors' experiences， this study explores innovative pathways for deep integration of modern information technologies with famous doctors' experiences from key dimensions， including data authenticity assurance， interdisciplinary collaboration mechanisms， and the establishment of dynamic inheritance standards. It proposes a paradigm shift in the inheritance of TCM famous doctors' experiences in the AI era， aiming to build a new TCM inheritance system of "digital intelligence empowerment and cross-disciplinary innovation"， providing theoretical support and practical pathways for the inheritance of famous doctors' experiences in TCM.

BACKGROUND:There is a significant correlation between sperm DNA fragmentation index and fertilization,embryonic development potential,embryo implantation,miscarriage,and offspring safety.However,its clinical reference value is affected by many factors,resulting in extremely limited clinical significance.This study took live birth as the outcome,corrected other confounding factors through propensity score matching,constructed the best clinical cutoff value of sperm DNA fragmentation index and live birth,and conducted internal and external tests on it,which has good predictive value and clinical application efficiency. OBJECTIVE:To investigate the reference threshold and offspring short-term security of in vitro fertilization-embryo transfer sperm DNA fragmentation index based on live birth. METHODS:A total of 1 921 patients who received in vitro fertilization and embryo transfer in Changzhou Maternal and Child Health Area Hospital from May 2019 to May 2021 were selected.On the basis of tendency matching tolerance of 0.02 and propensity score matching of 1:1,540 cases were successfully matched in each live birth group and non-live birth group,and the model group was established.135 patients who received in vitro fertilization and embryo transfer in Nanxishan Hospital of Guangxi Zhuang Autonomous Region were selected as the external validation group.The optimal clinical cutoff value of sperm DNA fragmentation index for live birth was investigated by the receiver operating characteristic curve.The accuracy and clinical application efficacy of the cutoff value were evaluated by restricted cubic spline curve,standard curve,clinical decision curve,clinical impact curve and internal and external validation tests. RESULTS AND CONCLUSION:(1)The DNA fragmentation index of sperm in the non-live birth group was significantly higher than that in the live birth group and had a significant negative correlation with live birth(r=-0.444,P<0.001).(2)Receiver operating characteristic curve results showed that the optimal cut-off value of DNA fragmentation index for live birth was 24.33%;the area under the curve was 0.775(0.746,0.804);the specificity was 72.60%;the sensitivity was 78.90%,and the accuracy was 75.70%.(3)Restricted cubic spline curve fitting the results of Logistic regression showed that when the sperm DNA fragmentation index was greater than 24.57%,the risk of clinical non-live birth increased.(4)The probability of Logistic regression analysis results showed that sperm DNA fragmentation index was a risk factor for live birth[OR(95%CI)=0.916(0.904,0.928),P<0.001],and when sperm DNA fragmentation index was greater than 27.78%,the probability of clinical live birth would be less than 50%.With the increase of sperm DNA fragmentation index by 1 unit,the probability of a live birth fell by 8.4%.(5)Internal and external to the validation of the clinical cutoff value showed that the cutoff point had certain clinical predictive value and accuracy.(6)Clinical decision curve and clinical impact curve results exhibited that the prediction model based on the clinical cut-off value had the maximum clinical net benefit value when the threshold probability was 0.22-0.73,and the ratio of loss to gain within the threshold probability range was always less than 1,which confirmed that the prediction model had good clinical application effectiveness.(7)The results of sperm DNA fragmentation index and offspring short-term security analysis showed that sperm DNA fragmentation index had no significant differences with preterm birth,body weight,deformity and sex.(8)These findings suggest that the optimal clinical cut-off value of sperm DNA fragmentation index for in vitro fertilization-embryo transfer live birth was 24.33%.The established clinical prediction model has good differentiation,accuracy and clinical application effectiveness.Sperm DNA fragmentation index has no significant impact on offspring short-term security,but large samples and long-term follow-up evaluation are still needed.

BACKGROUND:It has been confirmed that the mixing of decellularized matrix and polymer electrospinning can not only improve the structural properties of fibers,but also preserve the biological decellularized of decellularized matrix.However,there is no relevant report on the preparation of skin tissue engineering scaffolds by electrospinning polyurethane and decellularized skin matrix. OBJECTIVE:To investigate the reparative effect of a decellularized skin matrix/polyurethane blended fibrous scaffold on rat skin defects. METHODS:Polyurethane electrospun fibrous scaffold and decellularized skin matrix/polyurethane blended fibrous scaffold were fabricated using the electrospinning technique.The fiber structure was observed under scanning electron microscope.Rat adipose mesenchymal stem cells were inoculated on two kinds of scaffolds respectively.The morphology of the scaffolds was observed under scanning electron microscope.Three full-thickness skin defects of 1 cm×1 cm were fabricated on the back of 10 SD rats.Polyurethane electrospun fibrous scaffolds(control group)and decellularized skin matrix/polyurethane blended fibrous scaffolds(experimental group)were implanted in two of the defects,and no material was implanted in the remaining defects(blank control group).The skin wound healing was observed at 1,2,and 3 weeks after operation.At 3 weeks after implantation,the wound was stained with hematoxylin and eosin and the scar area was calculated. RESULTS AND CONCLUSION:(1)Under scanning electron microscope,the two kinds of electrospun fibers were reticulated,and the rat adipose mesenchymal stem cells attached to the fibers on the two kinds of scaffolds,and the adhesion was good.(2)With the extension of the postoperative time,the skin wounds of each group gradually healed.By week 3 after the operation,the skin wounds of the experimental group and the control group were basically healed,and small ulcers could be seen on the wounds of the blank control group.Hematoxylin-eosin staining of skin wounds showed that the epidermal coverage of the wound was basically complete in the control group and the experimental group,and fibroblast growth and inflammatory cell infiltration could be seen in the dermis.In addition,the collagen fibers of the wound in the experimental group were abundant and arranged in a regular order,basically parallel to the epidermal surface.The wound epidermis of blank control group was still defective.The scar area of the experimental group was smaller than that of the other two groups(P<0.05,P<0.01).(3)These results indicate that the decellularized skin matrix/polyurethane blended fibrous scaffold can effectively repair full-thickness skin defects and improve scar formation in rats.

Objective To develop an artificial intelligence (AI)-driven lung cancer database by structuring and standardizing clinical data, enabling advanced data mining for lung cancer research, and providing high-quality data for real-world studies. Methods Building on the extensive clinical data resources of the Department of Thoracic Surgery at Peking Union Medical College Hospital, this study utilized machine learning techniques, particularly natural language processing (NLP), to automatically process unstructured data from electronic medical records, examination reports, and pathology reports, converting them into structured formats. Data governance and automated cleaning methods were employed to ensure data integrity and consistency. Results As of September 2024, the database included comprehensive data from 18 811 patients, encompassing inpatient and outpatient records, examination and pathology reports, physician orders, and follow-up information, creating a well-structured, multi-dimensional dataset with rich variables. The database’s real-time querying and multi-layer filtering functions enabled researchers to efficiently retrieve study data that meet specific criteria, significantly enhancing data processing speed and advancing research progress. In a real-world application exploring the prognosis of non-small cell lung cancer, the database facilitated the rapid analysis of prognostic factors. Research findings indicated that factors such as tumor staging and comorbidities had a significant impact on patient survival rates, further demonstrating the database’s value in clinical big data mining. Conclusion The AI-driven lung cancer database enhances data management and analysis efficiency, providing strong support for large-scale clinical research, retrospective studies, and disease management. With the ongoing integration of large language models and multi-modal data, the database’s precision and analytical capabilities are expected to improve further, providing stronger support for big data mining and real-world research of lung cancer.

Alzheimer’s disease (AD) is a central neurodegenerative disease characterized by progressive cognitive decline and memory impairment in clinical. Currently, there are no effective treatments for AD. In recent years, a variety of therapeutic approaches from different perspectives have been explored to treat AD. Although the drug therapies targeted at the clearance of amyloid β-protein (Aβ) had made a breakthrough in clinical trials, there were associated with adverse events. Neuroinflammation plays a crucial role in the onset and progression of AD. Continuous neuroinflammatory was considered to be the third major pathological feature of AD, which could promote the formation of extracellular amyloid plaques and intracellular neurofibrillary tangles. At the same time, these toxic substances could accelerate the development of neuroinflammation, form a vicious cycle, and exacerbate disease progression. Reducing neuroinflammation could break the feedback loop pattern between neuroinflammation, Aβ plaque deposition and Tau tangles, which might be an effective therapeutic strategy for treating AD. Traditional Chinese herbs such as Polygonum multiflorum and Curcuma were utilized in the treatment of AD due to their ability to mitigate neuroinflammation. Non-steroidal anti-inflammatory drugs such as ibuprofen and indomethacin had been shown to reduce the level of inflammasomes in the body, and taking these drugs was associated with a low incidence of AD. Biosynthetic nanomaterials loaded with oxytocin were demonstrated to have the capability to anti-inflammatory and penetrate the blood-brain barrier effectively, and they played an anti-inflammatory role via sustained-releasing oxytocin in the brain. Transplantation of mesenchymal stem cells could reduce neuroinflammation and inhibit the activation of microglia. The secretion of mesenchymal stem cells could not only improve neuroinflammation, but also exert a multi-target comprehensive therapeutic effect, making it potentially more suitable for the treatment of AD. Enhancing the level of TREM2 in microglial cells using gene editing technologies, or application of TREM2 antibodies such as Ab-T1, hT2AB could improve microglial cell function and reduce the level of neuroinflammation, which might be a potential treatment for AD. Probiotic therapy, fecal flora transplantation, antibiotic therapy, and dietary intervention could reshape the composition of the gut microbiota and alleviate neuroinflammation through the gut-brain axis. However, the drugs of sodium oligomannose remain controversial. Both exercise intervention and electromagnetic intervention had the potential to attenuate neuroinflammation, thereby delaying AD process. This article focuses on the role of drug therapy, gene therapy, stem cell therapy, gut microbiota therapy, exercise intervention, and brain stimulation in improving neuroinflammation in recent years, aiming to provide a novel insight for the treatment of AD by intervening neuroinflammation in the future.

OBJECTIVE To investigate the effects of jaceosidin on osteoarthritis （OA） of rats by regulating adenosine monophosphate-activated protein kinase （AMPK）/NOD-like receptor protein 3 （NLRP3） pathway. METHODS Rats were randomly separated into OA group， jaceosidin group （33.33 mg/kg）， AMPK inhibitor （Compound C， 20 mg/kg） group， jaceosidin （33.33 mg/kg）+Compound C （20 mg/kg） group， and sham operation group， with 12 rats in each group. Except for the sham operation group， the OA model was induced with modified Hulth method in all other groups. After successful modeling， they were given a relevant dose of jaceosidin or normal saline intragastrically， and Compound C or normal saline intraperitoneally， once a day， for consecutive 8 weeks. Twenty-four h after the last medication， the degree of knee joint swelling in rats from each group was measured. The pathological changes of the articular cartilage tissue in the knee joints， and the Mankin score were assessed. The levels of tumor necrosis factor-α （TNF-α）， interleukin-18 （IL-18）， and IL-6， as well as the protein expressions of collagen Ⅱ， aggrecan （ACAN）， and a disintegrin and metalloproteinase with thrombospondin 5 （ADAMTS5）， phosphorylated AMPK （p-AMPK）， AMPK， NLRP3， cleaved-caspase-1， and cleaved-IL-1β were detected in the articular cartilage tissue of rats’ knees. RESULTS Compared with OA group， the cartilage tissue defect of jaceosidin group was relieved， the cartilage matrix staining was deepened， and the number of chondrocytes was increased. Knee swelling， Mankin score， the levels of TNF- α， IL-18 and IL-6， and protein expressions of ADAMTS5， NLRP3， cleaved-caspase-1 and cleaved-IL-1β in knee cartilage were significantly decreased or down-regulated. Protein expressions of collagen Ⅱ， ACAN and phosphorylation level of AMPK were significantly increased or up-regulated （P＜0.05）. Compound C significantly reversed the improvement effects of jaceosidin on the above indexes of OA rats （P＜0.05）. CONCLUSIONS Jaceosidin may inhibit inflammation and extracellular matrix degradation in OA rats by regulating the AMPK/NLRP3 signaling pathway.

Background Metals and metalloids in fine particulate matter (PM_2.5) may cause damage to the respiratory and circulatory systems of the human body, and long-term exposure is prone to causing chronic poisoning, cancer, and other adverse effects. Objective To assess the distribution characteristics of metals and metalloids in outdoor PM_2.5 in a southern city of China, conduct source apportionment, and evaluate the associated health risks, thereby providing theoretical support for further pollution control measures. Methods PM_2.5 samples were collected in districts A, B, and C of a southern China city, and the concentrations of 17 metals and metalloids were detected by inductively coupled plasma-mass spectrometry (ICP-MS). Pollution sources were assessed through enrichment factor and principal components analysis, and the main pollution sources were quantified using absolute principal component scores-multivariate linear regression (APCS-MLR). Health risks were evaluated based on the Technical guide for environmental health risk assessment of chemical exposure (WS/T777—2021). Results The ambient air PM_2.5 concentrations in the city were higher in winter and spring, and lower in summer and autumn. The annual average concentrations of ambient PM_2.5 in districts A, B, and C were 36.7, 31.9, and 24.4 μg·m⁻³, respectively. The ambient PM_2.5 levels in districts B and C were below the second-grade limit set by the Ambient air quality standards (GB 3095—2012). The enrichment factors of cadmium (Cd), aluminum (Al), and antimony (Sb) were greater than 10, those of copper (Cu), lead (Pb), arsenic (As), nickel (Ni), mercury (Hg), and molybdenum (Mo) fell between 1 and 10, and those of manganese (Mn), vanadium (V), chromium (Cr), cobalt (Co), barium (Ba), beryllium (Be), and uranium (U) were below or equal to 1. The comprehensive evaluation of source analysis showed that the main pollution sources in districts A and C and the whole city were coal-burning. In district B, the main pollution source was also coal combustion, followed by industrial process sources and dust sources. The carcinogenic risks of As and Cr were between 1×10⁻⁶ and 1×10⁻⁴. However, the hazard quotients for 15 metals and metalloids in terms of non-carcinogenic risk were below 1. Conclusion Cr and As in the atmospheric PM_2.5 of the city present a certain risk of cancer and should be paid attention to. In addition, preventive control measures should be taken against relevant pollution sources such as industrial emission, dust, and coal burning.

The general models for intermediates quality analysis in the production process of Yaobitong capsule were established by near infrared spectroscopy (NIRS) combined with chemometrics, realizing the rapid determination of notoginsenoside R1, ginsenoside Rg1, ginsenoside Re, ginsenoside Rb1, ginsenoside Rd and moisture. The spray-dried fine powder and total mixed granule were selected as research objects. The contents of five saponins were determined by high performance liquid chromatography and the moisture content was determined by drying method. The measured contents were used as reference values. Meanwhile, NIR spectra were collected. After removing abnormal samples by Monte Carlo cross validation (MCCV), Monte Carlo uninformative variables elimination (MC-UVE) and competitive adaptive reweighted sampling (CARS) were used to select feature variables respectively. Based on the feature variables, quantitative models were established by partial least squares regression (PLSR), extreme learning machine (ELM) and ant lion optimization least squares support vector machine (ALO-LSSVM). The results showed that CARS-ALO-LSSVM model had the optimum effect. The correlation coefficients of the six index components were greater than 0.93, and the relative standard errors were controlled within 6%. ALO-LSSVM was more suitable for a large number of samples with rich information, and the prediction effect and stability of the model were significantly improved. The general models with good predicting effect can be used for the rapid quality determination of Yaobitong capsule intermediates.