Objective To construct a prediction model for the population with hypertension and diabetes to assess the risk of chronic kidney disease (CKD), and to provide a scientific basis for formulating targeted CKD prevention and control measures. Methods Based on physical examination data from community residents aged 60 years and above in Nanjing in 2022, 10 221 patients with hypertension and diabetes were selected as the study subjects. Variables associated with CKD prevalence were screened using univariate analysis, and further variable selection was performed using LASSO regression. Finally, a CKD risk prediction model was constructed based on logistic regression. The model's performance was evaluated using the ROC curve and calibration curve. Results The prevalence rate of CKD in the study population was 22.71%, with a mean age of 71.66 years. LASSO regression identified seven variables associated with CKD: age, blood urea nitrogen (BUN), hemoglobin, uric acid, triglyceride-glucose (TyG) index, urine protein-to-creatinine ratio (UPCR), and medical insurance type. The final logistic regression model incorporated six variables: age [OR=1.067 (95% CI: 1.058-1.076)], BUN [OR=1.377 (95% CI: 1.338-1.418)], hemoglobin [OR=0.992 (95% CI: 0.989-0.995)], uric acid [OR=1.004 (95% CI: 1.003-1.004)], TyG index [OR=1.445 (95% CI: 1.324-1.577)], and self-payment medical insurance [OR=1.732 (95% CI: 1.542-1.945)]. The model had an AUC of 0.759 (95% CI: 0.747-0.770) and a Brier score of 0.140 (95% CI: 0.136-0.145), indicating good predictive performance. The calibration curve showed good agreement between the predicted risk and the observed value. Conclusion The constructed LASSO-logistic regression risk prediction model in this study can effectively assess the risk of CKD in elderly individuals aged 60 years and above with hypertension and diabetes, providing a basis for early identification of high-risk individuals and the formulation of targeted CKD prevention and control measures.

We have isolated an intestinal probiotic strain, Pediococcus acidilactici HRQ-1. To improve its gastrointestinal fluid tolerance, transportation and storage stability, and slow-release properties, we employed the extrusion method to prepare the microcapsules with P. acidilactici HRQ-1 as the core material and sodium alginate and chitosan as the wall material. The optimal conditions for preparing the microcapsules were determined by single factor and orthogonal tests, and the optimal ratio was determined by taking the embedding rate, survival rate, storage stability, gastrointestinal fluid tolerance, and release rate as the evaluation indexes. The results showed that under the optimal embedding conditions, the embedding rate reached (89.60±0.02)%. Under the optimal formula of freeze-drying protective agent, the freeze-drying survival rate reached (76.42±0.13)%, and the average size of the microcapsules produced was (1.16±0.03) mm. The continuous gastrointestinal fluid simulation experiments confirmed that the microcapsules ensured the viable bacterial count and can slowly release bacteria in the intestinal fluid. The curve of the viable bacterial count during storage at 4 ℃ and room temperature indicated that the prepared microcapsules achieved strains' live number protection. The formula and preparation process of P. acidilactici microcapsules may provide a technological reserve for the preparation of more live bacterial drugs in the future.
Pediococcus acidilactici/chemistry* ; Probiotics/chemistry* ; Capsules/chemistry* ; Alginates/chemistry* ; Chitosan/chemistry* ; Drug Compounding/methods* ; Glucuronic Acid/chemistry* ; Hexuronic Acids/chemistry* ; Freeze Drying

Objective To screen for key genes and important pathways common for different etiologies of acute kidney injury(AKI)by pathway-associated deep neural network and multiple machine learning algorithms.Methods AKI microarray datasets GSE30718,GSE37838,GSE53769,GSE108113,GSE125779,GSE99325,and GSE174020 downloaded from the Gene Expression Omnibus(GEO)database were merged,including 60 kidney samples from AKI patients and 79 kidney samples from healthy controls.They were divided(8∶2)into training sets and test sets,and were used to train and evaluate pathway-associated deep neural network and 4 machine learning algorithms,including least absolute shrinkage and selection operator(LASSO),random forest(RF),support vector machine-recursive feature elimination(SVM-RFE),and extreme gradient boosting(XgBoost),to screen for common key genes and pathways of different etiologies of AKI.The downloaded datasets GSE99340 and GSE1563 were merged,including 43 kidney samples from AKI patients and 36 kidney samples from healthy controls,which were used as external validation sets for LASSO model and nomogram performance test based on the final screened genes.The pathway-associated deep neural network and machine learning algorithms were evaluated using receiver operating characteristic curves,precision,recall,accuracy,and F1-score.The immune cell infiltration characteristics were explored in AKI via cell-type identification by estimating relative subsets of RNA transcripts(CIBERSORT),and Pearson correlation coefficients were used to evaluate the correlation between the final screened common key genes and immune cell infiltration levels.Results The pathway-associated deep neural network trained by 5-fold cross validation produced an area under curve(AUC)of 0.914 5±0.007 0,a precision of 0.750 0±0.044 0,a recall of 0.923 1±0.048 0,an accuracy of 0.838 7±0.016 0,and an F1-score of 0.827 6±0.020 0 in the test set,yielding a robust and highly accurate classification performance for AKI,and identified key pathways and a subset of candidate genes.The 4 machine learning algorithms all achieved high discriminative performance for AKI in the test set with AUC≥0.860,precision≥0.750,recall≥0.800,and F1-score≥0.774,and screened 7 common key genes for AKI with different etiologies,including CD86,C-X-C motif chemokine ligand 10(CXCL10),dynamin 2(DNM2),proto-oncogene FOS,transcription factor 12(TCF12),VGF nerve growth factor inducible(VGF),and A kinase anchoring protein 5(AKAP5).Based on the final screened common key genes,the LASSO model had an AUC of 0.940 4 for the test set and an AUC of 0.944 4 for the external validation,and the model showed a very high discriminatory ability for the AKI,which demonstrated the overall regulatory performance of the genes.The nomogram constructed based on the screened 7 genes demonstrated the highest classification performance with an AUC of 0.928 9,validating the outstanding contribution and overall action performance of the screened individual genes.Immune cell infiltration analysis showed that there were significant differences in B cells na?ve,mast cells activated,monocytes,macrophages M1,B cells memory,and dendritic cells activated between AKI samples and healthy control samples(all P＜0.05).Macrophages M1 and monocytes were positively correlated with CD86 and CXCL10,mast cells activated were positively correlated with FOS,and B cells na?ve were negatively correlated with CD86 and CXCL10(all P＜0.01).Mast cells activated were positively correlated with VGF and negatively correlated with CD86 and TCF12,while memory B cells were positively correlated with CD86(all P＜0.05).Conclusion Strategy combining pathway-associated deep neural network and multiple machine learning classifiers can mine high-value key genes from high-dimensional,complex and heterogeneous transcriptomic data as potential targets for therapeutic interventions in AKI.

Objective The kinetic parameters of Lin's squeezing-pressing adjustment manipulation were collected for the analysis of its mechanical characteristics,thus to establish a standardized operating procedure to guide clinical teaching of this technique.Methods Ten healthy male trainees(aged 20-30 years)from the Tuina Department of Guangdong Provincial of Chinese Medicine were selected as the subjects.A multi-point thin-film pressure testing system was used to collect mechanical parameters during the operation of Lin's squeezing-pressing adjustment manipulation.The mechanical characteristics were analyzed,and then a mathematical model of time-mechanics curve was constructed.Results(1)The kinetic parameters of Lin's squeezing-pressing adjustment manipulation were as follows:preload force averaged(279.45±19.36)N with a duration of(0.98±0.03)s,the valley value of preload force averaged(137.45±3.59)N,the maximum impact force was(495.56±7.33)N,the impact duration averaged(0.15±0.01)s,the impact velocity averaged(3 183.96±94.76)N/s,and the impulse was(57.16±1.82)N/s.(2)The fitting function of impact force showed large absolute values for both ascending and descending slopes,and the ascending slope was significantly greater than the descending slope,indicating that the Lin's manipulation stressed on rapid outburst of the strength and withdrawal of the strength.(3)One-way ANOVA revealed no statistically significant differences in the preload force and its duration and valley value,the maximum impact force,and impact time among different operators(P＞0.05).Conclusion The analysis of kinetic parameters demonstrates that skilled operators maintain relatively stable mechanical parameters when performing Lin's squeezing-pressing adjustment manipulation.This study provides a preliminary digital analysis of the mechanical characteristics of Lin's bone-setting manipulation in addressing"bone misalignment and tendon displacement",which supplies objective evaluation criteria for the technique.

177Lu-prostate specific membrane antigen(PSMA)radio-ligand therapy has been approved abroad for advanced prostate cancer and has been in several clinical trials in China.Based on domestic clinical practice and experimental data and referred to international experience and viewpoints,the expert group forms a consensus on the clinical application of 177Lu-PSMA radio-ligand therapy in prostate cancer to guide clinical practice.

Objective To construct a machine learning prediction model for postoperative liver injury in patients with non-liver surgery based on preoperative and intraoperative medication indicators.Methods A case-control study was conducted on 315 patients with liver injury after non-liver surgery selected from the databases developed by 3 large general hospitals from January 2014 to September 2022.With the positive/negative ratio of 1 ∶3,928 cases in corresponding period with non-liver surgery and without liver injury were randomly matched as negative control cases.These 1243 patients were randomly divided into the modeling group(n=869)and the validation group(n=374)in a ratio of 7∶3 using the R language setting code.Preoperative clinical indicators(basic information,medical history,relevant scale score,surgical information and results of laboratory tests)and intraoperative medication were used to construct the prediction model for liver injury after non-liver surgery based on 4 machine learning algorithms,k-nearest neighbor(KNN),support vector machine linear(SVM),logic regression(LR)and extreme gradient boosting(XGBoost).In the validation group,receiver operating characteristic(ROC)curve,precision-recall curve(P-R),decision curve analysis(DCA)curve,Kappa value,sensitivity,specificity,Brier score,and F1 score were applied to evaluate the efficacy of model.Results The model established by 4 machine learning algorithms to predict postoperative liver injury after non-liver surgery was optimal using the XGBoost algorithm.The area under the receiver operating characteristic curve(AUROC)was 0.916(95%CI:0.883～0.949),area under the precision-recall curve(AUPRC)was 0.841,Brier score was 0.097,and sensitivity and specificity was 78.95%and 87.10%,respectively.Conclusion The postoperative liver injury prediction model for non-liver surgery based on the XGBoost algorithm has effective prediction for the occurrence of postoperative liver injury.

In recent years,there have been significant advancements in tumor immunotherapy.Immune checkpoint inhibitors have emerged as a pivotal approach for treating advanced malignant tumors.The use of immunotherapy has been widely recommended and applied in clinical treatment both domestically and internationally.However,its clinical treatment efficacy still falls short of expectations.Improving the efficacy of immunotherapy for patients with advanced malignant tumors is currently a prominent research focus.Studies indicate that the combined use of immune enhancers and immune checkpoint inhibitors in various advanced malignant tumors significantly enhances the outcomes of immunotherapy.This article primarily highlights the combined application of immune enhancers and immune checkpoint inhibitors in cancer therapy,offering insights into their potential in the field of oncology treatment.

Cryoablation therapy with explicit anti-tumor mechanisms and histopathological manifestations has a long history.A large number of clinical practice has shown that cryoablation therapy is safe and effective,making it an ideal tumor treatment method in theory.Previously,its efficacy and clinical application were constrained by the limitations of refrigerants and refrigeration equipment.With the development of the new generation of cryoablation equipment represented by argon helium knives,significant progress has been made in refrigeration efficien-cy,ablation range,and precise temperature measurement,greatly promoting the progression of tumor cryoablation technology.This consensus systematically summarizes the mechanism of cryoablation technology,indications for oral mucosal melanoma(OMM)cryotherapy,clinical treatment process,adverse reactions and management,cryotherapy combination therapy,etc.,aiming to provide reference for carrying out the standardized cryoablation therapy of OMM.

Objective:To study clinical outcomes, genetic etiology, efficacy and safety of continuous renal replacement therapy (CRRT) for neonatal hyperammonemia.Methods:From September 2016 to June 2023, neonates with hyperammonemia receiving CRRT in NICU of our hospital were retrospectively analyzed. Their perinatal conditions, clinical manifestations, laboratory results, genetic tests, treatments and outcomes were collected. The patients were assigned into survival group and death group according to their conditions at discharge. SPSS 22.0 statistical software was used to analyze the differences between the two groups.Results:A total of 10 patients were enrolled, including 8 males and 2 females. The gestational age was 39.3(38.2,39.8)weeks and birth weight 3 300(3 050, 3 583) g. The age of onset was 2.0(2.0, 4.3) d. The main clinical manifestations included seizures, coma and high blood ammonia level (up to 586-1 250 μmol/L). The patients received CRRT at 3.0(2.0, 8.3) d of age and CRRT lasted for 20.5(16.5, 42.8) h. Before CRRT, average time of coma was 10.0(3.5, 12.8) h and the total duration of coma was 20.5(12.5, 29.0) h. After CRRT, blood ammonia decreased (52.6±22.2) μmol/L every hour for 6 h. The genetic tests showed ornithine transcarbamylase deficiency in 5 cases, methylmalonic acidemia in 2 cases, propionic acidemia in 1 case, carnitine acylcarnitine translocase deficiency in 1 case and transient hyperammonemia in 1 case. 6 patients survived. 4 patients died at discharge, including 2 withdrawal treatment. The duration of coma before CRRT and the total duration of coma in the death group were significantly longer than the survival group ( P<0.05). Conclusions:Inborn metabolic error are common causes of neonatal hyperammonemia. Timely CRRT can safely and effectively reduce blood ammonia levels and may improve clinical outcomes.