AIM: To investigate the predictive value of serum histone deacetylase 1(HDAC1)and endothelial nitric oxide synthase(eNOS)for infectious endophthalmitis after cataract surgery.METHODS: A total of 362 cataract patients(362 eyes)admitted to our hospital from January 2020 to January 2023 were selected as the research objects. According to the occurrence of postoperative infectious endophthalmitis, they were divided into infection group(15 cases, 15 eyes)and non-infection group(347 cases, 347 eyes). Enzyme linked immunosorbent assay(ELISA)was applied to detect the levels of serum HDAC1 and eNOS in all subjects, and the levels of serum HDAC1 and eNOS in both groups were compared; the influencing factors of infectious endophthalmitis were analyzed by multivariate Logistic regression; the receiver operative curve(ROC)was applied to analyze the predictive value of serum HDAC1 and eNOS levels for postoperative infectious endophthalmitis in cataract patients.RESULTS: The levels of serum HDAC1 and eNOS in the infected group were obviously higher than those in the uninfected group(all P<0.01). Surgical time, vitreous overflow, HDAC1, and eNOS were all risk factors for postoperative infectious endophthalmitis(all P<0.05). ROC results showed that the AUC of HDAC1 and eNOS in predicting postoperative infectious endophthalmitis in cataract patients was 0.878 and 0.877, respectively, with sensitivity of 88.7% and 87.7%, specificity of 70.4% and 7.8%, respectively, while the AUC of the two combination in predicting postoperative infectious endophthalmitis in cataract patients was 0.978, with a sensitivity of 86.7% and a specificity of 85.3%.CONCLUSION:The serum levels of HDAC1 and eNOS in patients with infectious endophthalmitis after cataract surgery are obviously increased, and the combined detection of serum HDAC1 and eNOS can improve the predictive efficacy of infectious endophthalmitis in cataract patients after surgery. Both can provide reference for clinical diagnosis and treatment.

AIM: To investigate the predictive value of serum histone deacetylase 1(HDAC1)and endothelial nitric oxide synthase(eNOS)for infectious endophthalmitis after cataract surgery.METHODS: A total of 362 cataract patients(362 eyes)admitted to our hospital from January 2020 to January 2023 were selected as the research objects. According to the occurrence of postoperative infectious endophthalmitis, they were divided into infection group(15 cases, 15 eyes)and non-infection group(347 cases, 347 eyes). Enzyme linked immunosorbent assay(ELISA)was applied to detect the levels of serum HDAC1 and eNOS in all subjects, and the levels of serum HDAC1 and eNOS in both groups were compared; the influencing factors of infectious endophthalmitis were analyzed by multivariate Logistic regression; the receiver operative curve(ROC)was applied to analyze the predictive value of serum HDAC1 and eNOS levels for postoperative infectious endophthalmitis in cataract patients.RESULTS: The levels of serum HDAC1 and eNOS in the infected group were obviously higher than those in the uninfected group(all P<0.01). Surgical time, vitreous overflow, HDAC1, and eNOS were all risk factors for postoperative infectious endophthalmitis(all P<0.05). ROC results showed that the AUC of HDAC1 and eNOS in predicting postoperative infectious endophthalmitis in cataract patients was 0.878 and 0.877, respectively, with sensitivity of 88.7% and 87.7%, specificity of 70.4% and 7.8%, respectively, while the AUC of the two combination in predicting postoperative infectious endophthalmitis in cataract patients was 0.978, with a sensitivity of 86.7% and a specificity of 85.3%.CONCLUSION:The serum levels of HDAC1 and eNOS in patients with infectious endophthalmitis after cataract surgery are obviously increased, and the combined detection of serum HDAC1 and eNOS can improve the predictive efficacy of infectious endophthalmitis in cataract patients after surgery. Both can provide reference for clinical diagnosis and treatment.

ObjectiveTomatoes are one of the highest-yielding and most widely cultivated economic crops globally, playing a crucial role in agricultural production and providing significant economic benefits to farmers and related industries. However, early blight in tomatoes is known for its rapid infection, widespread transmission, and severe destructiveness, which significantly impacts both the yield and quality of tomatoes, leading to substantial economic losses for farmers. Therefore, accurately identifying early symptoms of tomato early blight is essential for the scientific prevention and control of this disease. Additionally, visualizing affected areas can provide precise guidance for farmers, effectively reducing economic losses. This study combines hyperspectral imaging technology with machine learning algorithms to develop a model for the early identification of symptoms of tomato early blight, facilitating early detection of the disease and visual localization of affected areas. MethodsTo address noise interference present in hyperspectral images, robust principal component analysis (RPCA) is employed for effective denoising, enhancing the accuracy of subsequent analyses. To avoid insufficient information representation caused by the subjective selection of regions of interest, the Otsu’s thresholding method is utilized to extract tomato leaves effectively from the background, with the average spectrum of the entire leaf taken as the primary object of study. Furthermore, a comprehensive spectral preprocessing workflow is established by integrating multivariate scatter correction (MSC) and standardization methods, ensuring the reliability and effectiveness of the data. Based on the processed spectral data, a discriminant model utilizing a linear kernel function support vector machine (SVM) is constructed, focusing on characteristic wavelengths to improve the model's discriminative capability. ResultsCompared to full-spectrum modeling, this approach results in an 8.33% increase in accuracy on the test set. After optimizing the parameters of the SVM model, when C=1.64, the accuracies of the training set and test set reach 91.67% and 94.44%, respectively, demonstrating a 1.19% increase in training set accuracy compared to the unoptimized model, while maintaining the same accuracy on the test set, effectively alleviating issues of underfitting. ConclusionThis study successfully establishes an early discriminant model for tomato early blight using hyperspectral imaging and achieves visualization of early symptoms. Experimental results indicate that the SVM discriminant model based on characteristic wavelengths and a linear kernel function can effectively identify early symptoms of tomato early blight. Visualization of these symptoms in terms of disease probability allows for a more intuitive detection of early diseases and timely implementation of corresponding control measures. This visual analysis not only enhances the efficiency of disease identification but also provides farmers with more straightforward and practical information, aiding them in formulating more reasonable prevention strategies. These research findings provide valuable references for the early identification and visualization of plant diseases, holding significant practical implications for monitoring, identifying, and scientifically preventing crop diseases. Future research could further explore how to apply this model to disease detection in other crops and how to integrate IoT technology to create intelligent disease monitoring systems, enhancing the scientific and efficient management of crops.

The Chinese Pharmacopoeia is the legal technical standard which should be followed during the research, production, use, and administration of drugs. At present, the new edition of the Chinese Pharmacopoeia is planned to be promulgated and implemented. This article summarizes and analyzes the main characteristics and the content of updates and amendments of the Chinese Pharmacopoeia 2025 Edition(Volume Ⅰ), to provide a reference for the correct understanding and accurate implementation the new edition of the pharmacopoeia.

AIM: To investigate the expression changes and significance of serum microRNA-34a-5p(miR-34a-5p)and silent information regulator T1(SIRT1)in patients with endophthalmitis after cataract surgery.METHODS: Patients with endophthalmitis after cataract surgery from May 2020 to May 2024 were selected as infection group(20 cases), and patients without endophthalmitis were selected as control group(74 cases). Serum SIRT1 levels were detected by ELISA; the serum level of serum miR-34a-5p was detected by qRT-PCR; the correlation between miR-34a-5p and SIRT1 was analyzed by Pearson method; Logistic regression was used to analyze the influencing factors of endophthalmitis after cataract operation. Receiver operating characteristic(ROC)curve was drawn to analyze the diagnostic value of serum miR-34a-5p and SIRT1 levels in postoperative endophthalmitis.RESULTS: Compared with the control group, the serum level of miR-34a-5p in the infected group was significantly increased(P<0.001), and the serum level of SIRT1 was significantly decreased(P<0.001). Correlation analysis showed that miR-34a-5p was negatively correlated with SIRT1(r=-0.421, P<0.001). Logistic multivariate regression analysis showed that miR-34a-5p was an independent risk factor affecting endophthalmitis infection after cataract surgery(OR=3.532, P<0.05), and SIRT1 was a protective factor affecting endophthalmitis infection after cataract surgery(OR=0.875, P<0.05). The ROC curve showed that the area under curve(AUC)of serum miR-34a-5p combined with SIRT1 in the diagnosis of postoperative endophthalmitis was 0.933(95%CI: 0.861-0.975).CONCLUSION:Serum levels of miR-34a-5p are highly expressed and SIRT1 is lowly expressed in postoperative endophthalmitis, they are closely related to the occurrence and development of endophthalmitis after cataract surgery.

AIM: To explore the value of changes in the serum expression levels of triggering receptor expressed on myeloid cells-1(TREM-1)and matrix metalloproteinase-9(MMP-9)in early clinical diagnosis of postoperative endophthalmitis in patients with cataract surgery.METHODS: A total of 21 patients who underwent cataract surgery with infectious endophthalmitis in our hospital from May 2021 to May 2023 were selected as the study subjects(endophthalmitis group), and another 100 patients who underwent cataract surgery without endophthalmitis were selected as the non-endophthalmitis group. The serum levels of TREM-1, MMP-9, and the expression levels of inflammatory factors such as interleukin-1β(IL-1β), IL-17, and tumor necrosis factors-α(TNF-α)were detected and compared between the endophthalmitis group and the non-endophthalmitis group. The correlation between TREM-1 and MMP-9 was analyzed by Pearson method. Logistic regression was applied to analyze the factors that affected the occurrence of endophthalmitis in cataract patients after surgery. Receiver operating characteristic(ROC)curve was applied to analyze the early clinical diagnostic efficacy of TREM-1 and MMP-9 levels for postoperative endophthalmitis in cataract patients.RESULTS: Compared with the non-endophthalmitis group after cataract surgery, the expression levels of serum TREM-1, MMP-9, and inflammatory factors IL-1β, IL-17, and TNF-α in the endophthalmitis group were obviously increased(all P<0.05), and the TREM-1 was positively correlated with MMP-9(r=0.389, P<0.001). Logistic regression results showed that elevated levels of serum TREM-1 and MMP-9 expression, and vitreous overflow were independent risk factors for postoperative endophthalmitis in cataract patients(all P<0.05). ROC curve showed that the area under the curve(AUC)of TREM-1, MMP-9, and their combination in diagnosing postoperative endophthalmitis in cataract patients was 0.845, 0.844, and 0.935, respectively, and the clinical efficacy of the combination of the two in early diagnosis of postoperative endophthalmitis in cataract patients was better than that of serum TREM-1 and MMP-9 alone(all P<0.05).CONCLUSION: The expression levels of serum TREM-1 and MMP-9 in patients with endophthalmitis after cataract surgery are abnormally elevated, and the combination of the two has high clinical application value in early diagnosis of endophthalmitis after cataract surgery.

OBJECTIVE: To evaluate the therapeutic effect of Yu Melody relaxation training (YMRT) combined with Jianpi Jieyu Decoction (JJD) in treating patients with insomnia disorders (ID). METHODS: In this randomized controlled study, 94 ID patients were included from Xiyuan Hospital, China Academy of Chinese Medical Sciences from September 2022 to January 2024. They were randomly assigned to the YMRT group (47 cases, YMRT plus JJD) and the control group (47 cases, oral JJD) using a random number table. Both treatment administrations lasted for 4 weeks, with a 2-week follow-up. The primary outcome was change in Insomnia Severity Index (ISI) scores from baseline to 4 weeks of intervention. Secondary outcomes included ISI response at week 4, as well as ISI, Patient Health Questionnaire-9 (PHQ-9), and Generalized Anxiety Disorder 7-item (GAD-7) scores at baseline and weeks 1, 2, 3, 4, and 6. Additionally, Pittsburgh Sleep Quality Index (PSQI) scores were evaluated at baseline and weeks 4 and 6. Adverse events (AEs) were recorded and compared between groups. RESULTS: Five patients in each group did not complete the protocol requirements. The overall dropout rate was 10.64%. The full analysis set included all 47 cases in each group. The ISI score decreased significantly at week 4 from baseline in the YMRT group compared with the control group, with a between-group difference of -3.2 points [95% confidence interval (CI): -5.08 to -1.34; P<0.05]. The ISI response at week 4 in the YMRT group was significantly higher than that in the control group (85.11% vs. 51.06%), with a between-group difference of 34.05% (95% CI: 13.77% to 50.97%; P<0.05). At week 6, the YMRT group demonstrated greater reductions from baseline than the control group, with between-group differences of -2.1 points (-95% CI: -3.49 to -0.64; P<0.05) for PHQ-9 scores, -3.5 points (95% CI: -5.21 to -1.85; P<0.05) for PSQI scores, and -1.9 points (95% CI: -3.47 to -0.28; P<0.05) for GAD-7 scores. Moreover, at weeks 4 and 6, the ISI and PSQI scores in the YMRT group were significantly lower than those in the control group (P<0.05); and at week 6, the PHQ-9 score in the YMRT group was significantly lower (P<0.05). There was no significant difference in the incidence rates of AEs between the two groups (8.51% vs. 4.26%, P>0.05). CONCLUSIONS YMRT combined with oral JJD could improve sleep quality and alleviate depressive and anxiety symptoms in patients with ID. This combined therapy was effective and safe, and its effect was superior to oral JJD alone. (Registration No. ChiCTR2200063884).
Humans ; Sleep Initiation and Maintenance Disorders/drug therapy* ; Drugs, Chinese Herbal/therapeutic use* ; Male ; Female ; Relaxation Therapy/methods* ; Middle Aged ; Adult ; Treatment Outcome ; Combined Modality Therapy

Peptide receptor radionuclide therapy (PRRT) with radiolabeled SSTR2 agonists is a treatment option that is highly effective in controlling metastatic and progressive neuroendocrine tumors (NETs). Previous studies have shown that an SSTR2 agonist combined with albumin binding moiety Evans blue (denoted as ¹⁷⁷Lu-EB-TATE) is characterized by a higher tumor uptake and residence time in preclinical models and in patients with metastatic NETs. This study aimed to enhance the in vivo stability, pharmacokinetics, and pharmacodynamics of ¹⁷⁷Lu-EB-TATE by replacing the maleimide-thiol group with a polyethylene glycol chain, resulting in a novel EB conjugated SSTR2-targeting radiopharmaceutical, ¹⁷⁷Lu-LNC1010, for PRRT. In preclinical studies, ¹⁷⁷Lu-LNC1010 exhibited good stability and SSTR2-binding affinity in AR42J tumor cells and enhanced uptake and prolonged retention in AR42J tumor xenografts. Thereafter, we presented the first-in-human dose escalation study of ¹⁷⁷Lu-LNC1010 in patients with advanced/metastatic NETs. ¹⁷⁷Lu-LNC1010 was well-tolerated by all patients, with minor adverse effects, and exhibited significant uptake and prolonged retention in tumor lesions, with higher tumor radiation doses than those of ¹⁷⁷Lu-EB-TATE. Preliminary PRRT efficacy results showed an 83% disease control rate and a 42% overall response rate after two ¹⁷⁷Lu-LNC1010 treatment cycles. These encouraging findings warrant further investigations through multicenter, prospective, and randomized controlled trials.

HDAC7, a member of class IIa HDACs, plays a pivotal regulatory role in tumor, immune, fibrosis, and angiogenesis, rendering it a potential therapeutic target. Nevertheless, due to the high similarity in the enzyme active sites of class IIa HDACs, inhibitors encounter challenges in discerning differences among them. Furthermore, the substitution of key residue in the active pocket of class IIa HDACs renders them pseudo-enzymes, leading to a limited impact of enzymatic inhibitors on their function. In this study, proteolysis targeting chimera (PROTAC) technology was employed to develop HDAC7 drugs. We developed an exceedingly selective HDAC7 PROTAC degrader B14 which showcased superior inhibitory effects on cell proliferation compared to TMP269 in various diffuse large B cell lymphoma (DLBCL) and acute myeloid leukemia (AML) cells. Subsequent investigations unveiled that B14 disrupts BCL6 forming a transcriptional inhibition complex by degrading HDAC7, thereby exerting proliferative inhibition in DLBCL. Our study broadened the understanding of the non-enzymatic functions of HDAC7 and underscored the importance of HDAC7 in the treatment of hematologic malignancies, particularly in DLBCL and AML.

In protein engineering, while computational models are increasingly used to predict mutation effects, their evaluations primarily rely on high-throughput deep mutational scanning (DMS) experiments that use surrogate readouts, which may not adequately capture the complex biochemical properties of interest. Many proteins and their functions cannot be assessed through high-throughput methods due to technical limitations or the nature of the desired properties, and this is particularly true for the real industrial application scenario. Therefore, the desired testing datasets, will be small-size (∼10-100) experimental data for each protein, and involve as many proteins as possible and as many properties as possible, which is, however, lacking. Here, we present VenusMutHub, a comprehensive benchmark study using 905 small-scale experimental datasets curated from published literature and public databases, spanning 527 proteins across diverse functional properties including stability, activity, binding affinity, and selectivity. These datasets feature direct biochemical measurements rather than surrogate readouts, providing a more rigorous assessment of model performance in predicting mutations that affect specific molecular functions. We evaluate 23 computational models across various methodological paradigms, such as sequence-based, structure-informed and evolutionary approaches. This benchmark provides practical guidance for selecting appropriate prediction methods in protein engineering applications where accurate prediction of specific functional properties is crucial.