Objective To construct and evaluate a prognostic model for clear cell renal cell carcinoma(ccRCC)based on preoperative lipid ratios, cM0(i+)staging, and other clinical characteristics, so as to provide a precise tool for clinical prognosis assessment. Methods A retrospective analysis was conducted on the clinical data of 215 ccRCC patients treated in our hospital during May 2014 and May 2023. Lipid ratios were calculated using preoperative lipid data, and patients were divided into cM0(i+)stage or cM0 stage according to postoperative circulating tumor cells(CTCs)test results. The optimal lipid ratio and cutoff value were selected using receiver operating characteristic(ROC)curves and the X-tile method. Patients were subdivided into three groups based on the level of the low-density lipoprotein cholesterol to non-low-density lipoprotein cholesterol ratio(LNLR)and cM0(i+)staging:Group A [LNLR＞1.68 and diagnosed with cM0(i+)], Group B [LNLR＞1.68 or diagnosed with cM0(i+)], and Group C(LNLR ≤1.68 and diagnosed with cM0). Kaplan-Meier survival analysis was used to plot survival curves for patients in different groups. The log-rank test was employed to compare differences in recurrence-free survival(RFS)among the subgroups. Multivariate Cox regression analysis was conducted to identify the independent risk factors influencing RFS, and a nomogram prediction model was constructed based on these results. The predictive performance of the model was validated using ROC curves, calibration curves, and decision curves. Results ROC curves were plotted for lipid markers, and LNLR was identified as the most predictive for RFS. Its optimal cutoff point was 1. 68. Patients in Group A experienced a significantly shorter postoperative RFS. Multivariate Cox regression analysis identified preoperative LNLR, cM0(i+)staging, pathological grade, and stage as independent risk factors for RFS. A nomogram model was constructed based on these risk factors. The area under the ROC curve(AUC)for 1-, 3-, and 5-year RFS was 0.896(95% CI:0.8121-0.9627), 0.890(95%CI:0.7879-0.9641), and 0.870(95%CI:0.7697-0.9526), indicating good discriminatory ability and predictive performance. Calibration plots demonstrated good agreement between predicted and actual outcomes. Clinical decision curve analysis showed high clinical net benefit. Conclusion Preoperative LNLR level is an independent risk factor for RFS in ccRCC patients. The prognostic prediction model based on LNLR, cM0(i+)staging, patient pathological grade, and staging demonstrates good predictive performance for RFS and holds potential clinical application value.

OBJECTIVE To investigate of the regulatory effects of Budi Shenlian Recipe on gut microbiota and the phenotypic transition of tumor-associated macrophages(TAMs)in colorectal cancer(CRC)mice.METHODS Forty male BALB/c mice were divided into blank control group,model group,positive control group,high-dose Budi Shenlian Recipe group,and low-dose Budi Shenlian Recipe group(n=8).CRC-bearing models were established by subcutaneous injection of CT26 cells.Additionally,10 male BALB/c mice were divided into the Budi Shenlian Recipe fecal microbiota transplantation(BFMT)group and model fecal microbiota transplantation(MFMT)group(n=5).The gut microbiota of these mice was cleared using a mixed solution of quadruple antibiotics,followed by subcutaneous injection of CT26 cells to construct pseudo-germ-free CRC-bearing mice.Fecal samples from the model group and high-dose Budi Shenlian Recipe group were collected to prepare fecal microbiota solutions.The BFMT group received gavage with fecal microbiota solution from the high-dose Budi Shenlian Recipe group,while the MFMT group received gavage with fecal micro-biota solution from the model group.Tumor volume changes were observed and recorded.HE staining was used to assess pathological changes in tumor tissues.16S sequencing was performed to analyze changes in gut microbiota.Flow cytometry and immunofluorescence staining were used to evaluate the proportion of M1/M2 type TAMs in tumor tissues.ELISA was used to detect differences in TNF-α and IL-10 levels in tumor tissues.RESULTS Compared to the model group,the tumor volume of mice in the positive control group,high-dose Budi Shenlian Recipe group,and low-dose Budi Shenlian Recipe group grew more slowly(P<0.01).HE staining showed necrotic areas in tumor tissues and reduced mitotic figures in the positive control and Budi Shenlian Recipe groups compared to the model group.16S rRNA sequencing showed no significant differences in Chao1 and ACE indices between the high-dose Budi Shenlian Recipe group and the model group.PCoA analysis indicated a distinct microbial community structure between the blank group and mod-el group,with the microbial structure of CRC mice in the Budi Shenlian Recipe group closer to that of the blank group.Compared to the blank group,the model group showed a significant decrease in the proportion of Muribaculaceae,Muribaculum,Alloprevotella,and Prevotellaceae_UCG-001,and a significant increase in Lachnospiraceae_NK4A136_group,Bacteroides,and Helicobacter.After admin-istering Budi Shenlian Recipe to CRC mice,the community structure of some mice partially reverted to the level of the blank group.Transcriptome sequencing revealed that the most significant biological process(BP)among upregulated genes was the negative regulation of macrophage migration,suggesting that Budi Shenlian Recipe can reduce macrophage migration.Moreover,compared to the model group,the proportion of TAMs cells in the tumor tissues of the Budi Shenlian Recipe group significantly decreased(P<0.001).Simultaneously,compared to the model group,the proportion of M1 type TAMs in the Budi Shenlian Recipe group significant-ly increased(P<0.000 1),while the proportion of M2 type TAMs significantly decreased(P<0.05).Immunofluorescence analysis showed the same trend as flow cytometry.The content of TNF-α in tumor tissues of the Budi Shenlian Recipe group significantly in-creased(P<0.001),and IL-10 content significantly decreased(P<0.001).Additionally,compared to the MFMT group,the tumor volume in the BFMT group grew more slowly(P<0.0001).HE staining showed increased necrotic areas,sparser cell arrangement,and reduced pathological mitosis in the BFMT group.Furthermore,compared to the MFMT group,the proportion of TAMs cells in the tumor tissues of the BFMT group significantly decreased(P<0.01).Compared to the MFMT group,the proportion of M1 type TAMs cells in the BFMT group increased(P<0.000 1),while the proportion of M2 type TAMs cells decreased(P<0.01).Immunofluores-cence analysis further confirmed that Budi Shenlian Recipe fecal microbiota transplantation can reduce the proportion of TAMs in the tumor tissues of CRC mice and promote the conversion from M2 to M1 type,thereby reducing immune suppression in the tumor micro-environment.CONCLUSION Budi Shenlian Recipe can improve gut microbiota dysbiosis in CRC mice and exert anti-CRC effects by reducing tumor infiltration of TAMs and modulating the phenotypic transition of TAMs.There may be a certain correlation between these two effects.

The complement component 4 binding protein alpha(C4BPA)gene is the alpha chain of complement binding protein 4.As a plasma protein involved in the complement and coagulation systems,it can influence immune responses and lipid metabolism.In order to study the polymor-phism of C4BPA gene and its correlation with milk quality traits in Chinese Holstein cows,genom-ic DNA was extracted from blood samples of 92 Chinese Holstein cows,and the target fragment of C4BPA gene was amplified by PCR,and the association analysis was performed by using direct se-quencing to obtain the SNP loci and milk quality traits.The results showed that among the four SNPs found at the third intron of the C4BPA gene,I3-11 G＞A was highly significantly correlated with milk protein and urea nitrogen(P＜0.05),I3-291 T＞G was significantly correlated with lac-tose(P＜0.05),I3-374 C＞T was highly significantly correlated with lactose and urea nitrogen(P＜0.05),and I3-375 T＞G was highly significantly correlated with lactose(P＜0.05),milk pro-tein and urea nitrogen.The chi-square test values for each point indicated that the population was in genetic equilibrium.Individuals of haplotype combination H1 H1 had the highest lactose content,and haplotype combination H1H2 can be used as the best haplotype combination in the molecular selection work of dairy cows.

Drug development encompasses multiple processes,wherein protein subcellular localization is essential.It promotes target identification,treatment development,and the design of drug delivery systems.In this research,a deep learning framework called LocPro is presented for predicting protein subcellular localization.Specifically,LocPro is unique in(a)combining protein representations from the pre-trained large language model(LLM)ESM2 and the expert-driven tool PROFEAT,(b)implementing a hybrid deep neural network architecture that integrates convolutional neural network(CNN),fully connected(FC)layer,and bidirectional long short-term memory(BiLSTM)blocks,and(c)developing a multi-label framework for predicting protein subcellular localization at multiple granularity levels.Additionally,a dataset was curated and divided using a homology-based strategy for training and validation.Compar-ative analyses show that LocPro outperforms existing methods in sequence-based multi-label protein subcellular localization prediction.The practical utility of this framework is further demonstrated through case studies on drug target subcellular localization.All in all,LocPro serves as a valuable complement to existing protein localization prediction tools.The web server is freely accessible at https://idrblab.org/LocPro/.

Advancements in artificial intelligence(AI)and emerging technologies are rapidly expanding the exploration of chemical space,facilitating innovative drug discovery.However,the transformation of novel compounds into safe and effective drugs remains a lengthy,high-risk,and costly process.Comprehensive early-stage evaluation is essential for reducing costs and improving the success rate of drug development.Despite this need,no comprehensive tool currently supports systematic evaluation and efficient screening.Here,we present druglikeFilter,a deep learning-based framework designed to assess drug-likeness across four critical dimensions:1)physicochemical rule evaluated by systematic determination,2)toxicity alert investigated from multiple perspectives,3)binding affinity measured by dual-path analysis,and 4)compound synthesizability assessed by retro-route prediction.By enabling automated,multidimensional filtering of compound libraries,druglikeFilter not only streamlines the drug development process but also plays a crucial role in advancing research efforts towards viable drug candidates,which can be freely accessed at https://idrblab.org/drugfilter/.

Objective:To investigate the effect of blood lipids and statins use on the outcome of acute ischemic stroke (AIS) patients with cerebral microbleeds (CMBs).Methods:Consecutive AIS patients with CMBs hospitalized at the First People's Hospital of Taicang, Jiangsu Province from July 2023 to June 2024 were included retrospectively. At 3 months after onset, the modified Rankin Scale was used for outcome assessment. 0-2 was defined as good outcome and >2 was defined as poor outcome. Multivariate logistic regression analysis was used to identify independent influencing factors for poor outcome. Results:A total of 110 AIS patients with CMBs were enrolled, including 72 males (65.5%), aged 68.04±3.12 years. Thirty patients (27.3%) had poor outcome. Univariate analysis showed that age, baseline National Institutes of Health Stroke Scale (NIHSS) score, total cholesterol, triglycerides, low-density lipoprotein cholesterol, and the proportion of patients with hypertension and diabetes in poor outcome group were significantly higher than those in good outcome group, while baseline high-density lipoprotein cholesterol and the proportion of patients using statins before onset were significantly lower than those in good outcome group ( P<0.05). Multivariate logistic regression analysis showed that age (odds ratio [ OR] 1.309, 95% confidence interval [ CI] 1.007-1.702; P=0.044), the baseline NIHSS score ( OR 1.541, 95% CI 1.143-2.078; P=0.005) and high triglycerides ( OR 5.150, 95% CI 2.150-8.717; P=0.023) were the independent risk factors for poor outcome, while high high-density lipoprotein cholesterol ( OR 0.001, 95% CI 0.001-0.034; P<0.001) and statins use ( OR 0.231, 95% CI 0.046-0.558; P=0.019) were the independent protective factors for good outcome. Conclusions:Blood lipid and statins use are independent influencing factors for the outcome of AIS patients with CMBs. The use of statins before onset is associated with a lower risk of poor outcome in AIS patients with CMBs.

Advancements in artificial intelligence (AI) and emerging technologies are rapidly expanding the exploration of chemical space, facilitating innovative drug discovery. However, the transformation of novel compounds into safe and effective drugs remains a lengthy, high-risk, and costly process. Comprehensive early-stage evaluation is essential for reducing costs and improving the success rate of drug development. Despite this need, no comprehensive tool currently supports systematic evaluation and efficient screening. Here, we present druglikeFilter, a deep learning-based framework designed to assess drug-likeness across four critical dimensions: 1) physicochemical rule evaluated by systematic determination, 2) toxicity alert investigated from multiple perspectives, 3) binding affinity measured by dual-path analysis, and 4) compound synthesizability assessed by retro-route prediction. By enabling automated, multidimensional filtering of compound libraries, druglikeFilter not only streamlines the drug development process but also plays a crucial role in advancing research efforts towards viable drug candidates, which can be freely accessed at https://idrblab.org/drugfilter/.

Drug development encompasses multiple processes, wherein protein subcellular localization is essential. It promotes target identification, treatment development, and the design of drug delivery systems. In this research, a deep learning framework called LocPro is presented for predicting protein subcellular localization. Specifically, LocPro is unique in (a) combining protein representations from the pre-trained large language model (LLM) ESM2 and the expert-driven tool PROFEAT, (b) implementing a hybrid deep neural network architecture that integrates convolutional neural network (CNN), fully connected (FC) layer, and bidirectional long short-term memory (BiLSTM) blocks, and (c) developing a multi-label framework for predicting protein subcellular localization at multiple granularity levels. Additionally, a dataset was curated and divided using a homology-based strategy for training and validation. Comparative analyses show that LocPro outperforms existing methods in sequence-based multi-label protein subcellular localization prediction. The practical utility of this framework is further demonstrated through case studies on drug target subcellular localization. All in all, LocPro serves as a valuable complement to existing protein localization prediction tools. The web server is freely accessible at https://idrblab.org/LocPro/.

Objective:To investigate the predictive value of preoperative coagulation function and inflammation response biomarkers for postoperative recurrence of non-muscle-invasive bladder cancer (NMIBC) patients.Methods:The clinical data of 390 NMIBC patients underwent surgical treatment from May 2014 to May 2021 in the Second Affiliated Hospital of Xi′an Jiaotong University were retrospectively analyzed. The baseline characteristics coagulation function, inflammation response indexes and tumor characteristics were recorded. The baseline characteristics included gender, age and smoking history; the coagulation function included prothrombin time (PT), activated partial thromboplastin time (APTT), fibrinogen (FIB) and D-dimer; the inflammation response indexes included neutrophil count, lymphocyte count, platelet count and monocyte count, and the systemic inflammatory response index (SIRI) and systemic immune-inflammation index (SII) were calculated; tumor characteristics included TNM stage, pathological grade, tumor length, tumor amount and postoperative instillation drugs. The patients were followed up until May 2022, with recurrence records and grouping. The "pROC" package was used to draw the receiver operating characteristic (ROC) curve, and calculate the optimal cutoff values of biomarkers. Multivariate Cox regression analysis was used to analyze the independent risk factors of recurrence in patients with NMIBC (variables were selected with P<0.1). The nomogram and its calibration curve were drawn by the "survival" and "rms" packages, and the area under the curve (AUC) was calculated with the "pROC" package for assessing the predictive ability of the model. The "caret" package was used for ten-fold cross-validation to evaluate the external applicability of the nomogram. Results:The ROC curve analysis result showed that the optimal cutoff values of PT, APTT, FIB, D-dimer, SIRI and SII were 11.95 s, 17.65 s, 0.233 mg/L, 565 ng/L, 0.62 and 291.5, respectively. The 390 patients with NMIBC were followed up 29 to 71 months, with a median follow-up time of 49 months. Among them, 113 patients experienced postoperative recurrence (recurrence group), and the recurrence rate was 29.0%; while 277 patients did not experience recurrence (non-recurrence group). The rate of FIB≥0.233 mg/L, D-dimmer ≥565 ng/L, SIRI≥0.62 and SII≥291.5, T 1 stage, high-grade tumor, tumor length ≥2.3 cm and multiple tumor in recurrence group were significantly higher than those in non-recurrence group: 90.3% (102/113) vs. 71.5% (198/277), 33.6% (38/113) vs. 23.5% (65/277), 74.3% (84/113) vs. 56.7% (157/277), 84.1% (95/113) vs. 60.6% (168/277), 77.9% (88/113) vs. 38.6% (107/277), 25.7% (29/113) vs. 8.3% (23/277), 49.6% (56/113) vs. 32.1% (89/277) and 41.6% (47/113) vs. 19.9% (55/277), and there were statistical differences ( P<0.01 or <0.05); there were no statistical differences in gender ratio, age, smoking history, PT, APTT and postoperative instillation drugs between the two groups ( P>0.05). Multivariate Cox regression analysis result showed that FIB≥0.233 mg/L, SII≥291.5, T 1 stage, high pathological grade, tumor length≥2.3 cm and multiple tumor were independent risk factors of postoperative recurrence in patients with NMIBC ( HR = 2.186, 1.627, 3.182, 1.675, 1.775 and 2.052; 95% CI 1.149 to 4.159, 0.913 to 2.902, 1.988 to 5.095, 1.067 to 2.630, 1.208 to 2.608 and 1.388 to 3.033; P<0.1). A nomogram model was constructed to predict postoperative 1-, 3- and 5-year non-recurrence based on FIB, SII, T stage, tumor length, pathological grade and tumor amount. The calibration curve analysis result showed that the nomogram model predicted good consistency between the postoperative 1-, 3-, 5-year non-recurrence rates and the actual incidence rate in patients with NMIBC. ROC curve analysis result showed that the AUC of the nomogram model for predicting postoperative 1-, 3- and 5-year non-recurrence in patients with NMIBC were 0.746, 0.789 and 0.835 (95% CI 0.695 to 0.832, 0.703 to 0.875 and 0.756 to 0.915). The ten-fold cross-validation result showed that the nomogram model had good external applicability for predicting postoperative 1-, 3- and 5-year non-recurrence in patients with NMIBC, with AUC of 0.754, 0.781 and 0.832 (95% CI 0.689 to 0.817, 0.724 to 0.832 and 0.778 to 0.879). Conclusions:The nomogram model based on FIB, SII, T stage, tumor length, pathological grade and tumor amount can accurately predict the postoperative 1-, 3- and 5-year recurrence risks in patients with NMIBC. The model helps clinical doctors early identify high-risk recurrent NMIBC patients, and provides reference for the development of individualized treatment plans.

Purpose To explore the feasibility and clinical value of deep learning-based image reconstruction technology in 5.0T MRI for nasopharyngeal carcinoma.Materials and Methods A prospective study was conducted on 50 newly diagnosed nasopharyngeal carcinoma patients from August to December 2024 at Sun Yat-sen University Cancer Center.5.0T MRI was performed to scan the nasopharynx region.Routine scanning protocols included transverse T2WI,transverse T1WI,transverse contrast-enhanced T1WI and coronal fat-suppressed contrast-enhanced T1WI sequences.Based on these standard scanning protocols,DeepRecon deep learning reconstruction technology with different levels(grade 1-5)was applied,generating a total of 24 sets of images.Qualitative evaluation employed a Likert scale(5-point system)for subjective scoring on lesion detection,lesion edge clarity,artifacts and overall image quality.Quantitative evaluation was performed using the signal-to-noise ratio and contrast-to-noise ratio to objectively assess the quality of the 24 image sets.Differences in qualitative and quantitative indicators between different groups were compared,while the Kappa coefficient was used to analyze the consistency of subjective evaluations by two radiologists.Results In the qualitative assessment of 24 image sets from four MRI sequences(with and without DeepRecon reconstruction),DeepRecon images(grade 2-4)significantly outperformed traditional images in all features except for artifact reduction(Z=-12.11--6.23,all P<0.001).Images reconstructed at DeepRecon grade 3 had the highest overall score and the best image quality.Furthermore,compared with traditional images,DeepRecon images(grade 2-5)demonstrated significantly improved signal-to-noise ratio for both lesions and the lateral pterygoid muscle(t=-15.67--3.44,Z=-6.09--4.63,all P<0.01).In addition,in the transverse T2WI,transverse contrast-enhanced T1WI and coronal fat-suppressed contrast-enhanced T1WI images with DeepRecon reconstruction(grade 2-5),the contrast-to-noise ratio(lesion/lateral pterygoid muscle)also showed significant improvement compared to traditional images(t=-12.71--3.19,Z=-6.08--4.47,all P<0.001).The inter-observer agreement for the overall subjective quality score between the two radiologists was good(Kappa=0.75-0.82,all P<0.01).Conclusion DeepRecon deep learning reconstruction technology significantly increases the signal-to-noise ratio and resolution of traditional magnetic resonance images of nasopharyngeal cancer,improving image clarity and bringing more possibilities for the advancement of imaging diagnosis.