Cancer stem cells (CSCs) are widely acknowledged as primary mediators to the initiation and progression of tumors. The association between microbial infection and cancer stemness has garnered considerable scholarly interest in recent years. Porphyromonas gingivalis (P. gingivalis) is increasingly considered to be closely related to the development of oral squamous cell carcinoma (OSCC). Nevertheless, the role of P. gingivalis in the stemness of OSCC cells remains uncertain. Herein, we showed that P. gingivalis was positively correlated with CSC markers expression in human OSCC specimens, promoted the stemness and tumorigenicity of OSCC cells, and enhanced tumor formation in nude mice. Mechanistically, P. gingivalis increased lipid synthesis in OSCC cells by upregulating the expression of stearoyl-CoA desaturase 1 (SCD1) expression, a key enzyme involved in lipid metabolism, which ultimately resulted in enhanced acquisition of stemness. Moreover, SCD1 suppression attenuated P. gingivalis-induced stemness of OSCC cells, including CSCs markers expression, sphere formation ability, chemoresistance, and tumor growth, in OSCC cells both in vitro and in vivo. Additionally, upregulation of SCD1 in P. gingivalis-infected OSCC cells was associated with the expression of KLF5, and that was modulated by P. gingivalis-activated NOD1 signaling. Taken together, these findings highlight the importance of SCD1-dependent lipid synthesis in P. gingivalis-induced stemness acquisition in OSCC cells, suggest that the NOD1/KLF5 axis may play a key role in regulating SCD1 expression and provide a molecular basis for targeting SCD1 as a new option for attenuating OSCC cells stemness.
Porphyromonas gingivalis/pathogenicity* ; Stearoyl-CoA Desaturase/metabolism* ; Humans ; Carcinoma, Squamous Cell/pathology* ; Mouth Neoplasms/metabolism* ; Animals ; Neoplastic Stem Cells/microbiology* ; Mice, Nude ; Mice ; Nod1 Signaling Adaptor Protein/metabolism* ; Kruppel-Like Transcription Factors/metabolism* ; Cell Line, Tumor

Objective To evaluate the forensic application value of used dental floss as a source of bio-logical evidence for individual identification by analyzing the effects of dental floss sample collection methods,DNA extraction methods,preservation conditions,and sampling sites on the success rate of STR typing.Methods Dental floss samples were collected using three techniques:direct cutting,cotton swab wiping,and flocked swab wiping,respectively.DNA was extracted respectively by the Chelex,spin column-based and magnetic bead-based methods.DNA quantification and STR typing were per-formed using the Qubit kit and FGI HumDNA Typing kit(Platinum),respectively.Storage environ-ments(temperature and humidity,ultraviolet radiation)and sampling locations(the floss part,the handle part)on DNA quantity and STR typing were evaluated.Results Through conducting a statisti-cal analysis of three key indicators of average DNA mass concentration,STR locus detection rate,and typing accuracy rate,the direct cutting method demonstrated the highest efficacy,followed by cotton swab wiping mothed,and the flocked swab wiping method had the lowest efficacy.Direct cutting yielded an average DNA mass concentration greater than(4.94±1.87)ng/μL,with STR locus detection and accuracy rates of 100%.Bead-based DNA extraction method produced superior DNA concentration and quality compared to spin column-based and Chelex methods,regardless of whether the sampling technique used.Preservation conditions had a significant impact on the DNA analysis of samples.Par-ticularly,the STR typing accuracy of samples preserved at 55℃/50%RH for 35 days dropped to(81.82±12.31)%,and that of samples exposed to ultraviolet radiation for 12 h dropped to(55.46±34.31)%.DNA concentration from the handle part of dental floss was extremely low,with an STR typing accuracy of only(30.91±27.35)%.Conclusion Using cotton swabs to wipe or directly cutting the thread of dental floss samples,and combining this approach with the magnetic bead method for DNA extraction,can best guarantee the concentration and quality of DNA.In addition,samples should be stored in low-temperature,low-humidity environment,protected from light and ultraviolet radiation.

Objective To develop a predictive model for postoperative mortality risk in patients with acute aortic dissection(AAD)using the Extreme Gradient Boosting(XGBoost)algorithm combined with Shapley Additive Explanation(SHAP),and to establish a prediction website to serve as a diagnostic and therapeutic support platform for clinicians and patients.Methods A retrospective cohort study design was adopted.Data from 782 AAD patients who underwent surgical treatment at the Fourth Hospital of Hebei Medical University from January 2013 to December 2023 were collected,including basic information and initial serum biomarker test results.Patients were randomly divided into training and test sets at a 7:3 ratio.An external validation set consisting of 313 AAD patients admitted to the Second Hospital of Hebei Medical University from January 2020 to December 2023 was also established for further model validation.Variables were screened using LASSO regression,and an XGBoost machine learning model was constructed and interpreted using SHAP.The predictive performance of the model was evaluated using receiver operating characteristic(ROC)curve analysis.Using the Shiny package,the XGBoost model was deployed to shinyapps.io to create a prediction website for postoperative mortality risk in AAD patients.One patient was selected by simple random sampling from the test set and the external validation set respectively for the prediction example on the Shiny webpage.Results The XGBoost model demonstrated high predictive performance for postoperative mortality in AAD patients,with area under the ROC curve(AUC)values of 0.928(95%CI 0.901-0.956)in the training set,0.919(95%CI 0.891-0.949)in the test set,and 0.941(95%CI 0.915-0.967)in the external validation set.SHAP values indicated the following order of variable importance in the model(from highest to lowest):"lactate dehydrogenase""blood chlorine""multiple organ injury""carbon dioxide combining power""prothrombin time""α-hydroxybutyric acid""creatine kinase isoenzyme""Stanford classification""combined use of bedside blood purification""gender""acute kidney injury""gastrointestinal bleeding""brain injury"and"shock".A risk prediction website for adverse postoperative outcomes in AAD patients was developed using XGBoost-SHAP method(https://dun-dunxiaolu.shinyapps.io/document/)and validated with examples.One randomly selected patient from each of the test and external validation sets was applied:the predicted mortality risk value for patient 1(who died postoperatively)was 0.9539,and that for patient 2(who survived postoperatively)was 0.0206.Conclusions The XGBoost-SHAP model demonstrates high accuracy in predicting postoperative mortality risk for AAD patients.The online prediction tool established based on this model enhances the identification efficiency of high-risk postoperative mortality patients.

Objective To observe the clinical efficacy of self-formulated Shenqi Buwei Decoction(derived from Huangqi Renshen Decoction)in treating patients with stable chronic obstructive pulmonary disease(COPD)differentiated as lung-spleen qi deficiency syndrome.Methods A total of 110 patients with stable COPD differentiated as lung-spleen qi deficiency syndrome were randomly divided into a control group and an observation group,with 55 patients in each group.The control group was given Tiotropium Bromide Inhalation Powder for the inhalation treatment,and the observation group was given Shenqi Buwei Decoction on the basis of treatment for the control group,and the course of treatment covered 3 months.The changes of pulmonary function indicators of forced expiratory volume in one second(FEV1),forced vital capacity(FVC),and one-second rate of FEV1/FVC,modified Medical Research Council index(mMRC)dyspnea scores,6-minute walk test(6MWT),COPD Assessment Test(CAT)scores,and traditional Chinese medicine(TCM)syndrome scores in the two groups were observed before and after treatment.After treatment,the clinical efficacy and safety of the two groups were evaluated.Results(1)During the trial,one patient was excluded and two patients fell off from the observation group,and three patients fell off from the control group.Eventually,52 patients in each of the two groups were included in the efficacy statistics.(2)After 3 months of treatment,the total effective rate of the observation group was 80.77%(42/52)and that of the control group was 67.31%(35/52).The intergroup comparison(tested by chi-square test)showed that the therapeutic effect of the observation group was slightly superior to that of the control group,but the difference was not statistically significant(P＞0.05).(3)In terms of indexes,After treatment,the levels of pulmonary function indicators of FEV1,FEV1/FVC in the control group and FEV1,FVC,FEV1/FVC in the observation group were significantly improved compared with those before treatment(P＜0.05),and the improvement of FEV1,FVC,FEV1/FVC in the observation group was significantly superior to that in the control group(P＜0.05).(4)After treatment,the 6MWT,mMRC and CAT scores of the two groups were significantly improved compared with those before treatment(P＜0.05),and the improvement in the observation group was significantly superior to that in the control group(P＜0.05).(5)After treatment,the TCM syndrome scores of the two groups of patients were significantly decreased in comparison with those before treatment(P＜0.05),and the decrease in the observation group was significantly superior to that in the control group(P＜0.05).(6)During the treatment process,no obvious adverse reactions occurred in the two groups of patients,there were no abnormal changes in the safety indicators,either.Conclusion On the basis of conventional western medicine treatment,the combined use of Shenqi Buwei Decoction exerts certain efficacy in the treatment of patients with stable COPD differentiated as lung-spleen qi deficiency syndrome.The combined therapy can effectively improve the ventilation function,relieve the clinical symptoms,improve the quality of life and delay the decline of lung function of the patients.

Objective To preliminarily investigate the related factors influencing the size of vestibular schwannomas.Methods The clinical data of patients with vestibular schwannomas who underwent retrosigmoid approach surgery at the department of neurosurgery of First Affiliated Hospital of Xinjiang Medical University from June 2013 to June 2023 were retrospectively analyzed.The tumor size of the patients was evaluated based on their preoperative imaging data.Univariate and multiple linear regression analyses were performed to explore the factors affecting the size of vestibular schwannomas.Results The tumor size of patients was ranging from 0.63 to 6.60 cm,with a median size of 2.97(2.20,3.80)cm.Univariate analysis showed that gender(P=0.010),ethnicity(P=0.001),age(P=0.049)and cystic solid tumor(P<0.001)were related to the size of vestibular schwannomas.Large-sized vestibular schwannomas were most commonly cystic-solid,and small and medium-sized vestibular schwannomas were most commonly solid.BMI,surgical side and place of residence were not correlated with the size of vestibular schwannomas(P>0.05).Multiple linear regression results showed that male(B=0.390,P=0.001)and Uyghur(B=0.611,P<0.001)patients were more likely to develop large tumors;with every 1-year increase in age,the maximum diameter of the tumor was reduced by an average of 0.011 cm(B=-0.011,P=0.027).Conclusion The gender,age,and ethnicity of patients are correlated with the size of vestibular schwannomas,and male,Uyghur,or younger patients were at higher risk of developing larger vestibular schwannomas.

OBJECTIVE: To analyze the report status of outcomes and measurement instruments of randomized controlled trials (RCTs) of acupuncture for post-stroke dysphagia, so as to provide a basis for designing clinical trials and developing the core outcome set in acupuncture for post-stroke dysphagia. METHODS: RCTs of acupuncture for post-stroke dysphagia were searched in databases i.e. CNKI, SinoMed, Wanfang, PubMed, EMbase, Web of Science and clinical trial registries i.e. ClinicalTrials.gov and Chinese Clinical Trial Registry (ChiCTR), from January 1st, 2012 to October 30th, 2021. By literature screening and data extraction, outcomes and measurement instruments were summarized and analyzed. RESULTS: A total of 172 trials (including 165 RCTs and 7 ongoing trials registrations) were included, involving 91 outcomes. The outcomes could be classified into 7 domains according to functional attributes, namely clinical manifestation, physical and chemical examination, quality of life, TCM symptoms/syndromes, long-term prognosis, safety assessment and economic evaluation. It was found that there were various measurements instruments with large differences, inconsistent measurement time point and without discriminatively reporting primary or secondary outcomes. CONCLUSION The status quo of outcomes and measurement instruments of RCTs of acupuncture for post-stroke dysphagia is not conducive to the summary and comparison of each trial's results. Thus, it is suggested to develop a core outcome set for acupuncture for post-stroke dysphagia to improve the normative and research quality of their clinical trial design.
Humans ; Deglutition Disorders/therapy* ; Randomized Controlled Trials as Topic ; Acupuncture Therapy ; Databases, Factual ; Physical Examination ; Stroke/complications*

This study used machine learning techniques combined with routine blood cell analysis parameters to build preliminary prediction models, helping differentiate patients with depression from healthy controls, or patients with anxiety. A multicenter study was performed by collecting blood cell analysis data of Beijing Chaoyang Hospital and the First Hospital of Hebei Medical University from 2020 to 2021. Machine learning techniques, including support vector machine, decision tree, naïve Bayes, random forest and multi-layer perceptron were explored to establish a prediction model of depression. The results showed that based on the blood cell analysis results of healthy controls and depression group, the accuracy of prediction model reached as high as 0.99, F1 was 0.975. Receiver operating characteristic curve area and average accuracy were 0.985 and 0.967, respectively. Platelet parameters contributed mostly to depression prediction model. While, to random forest differential diagnosis model based on the data from depression and anxiety groups, prediction accuracy reached 0.68 and AUC 0.622. Age, platelet parameters, and average volume of red blood cells contributed the most to the model. In conclusion, the study researched on the prediction model of depression by exploring blood cell analysis parameters, revealing that machine learning models were more objective in the evaluation of mental illness.
Humans ; Depression ; Bayes Theorem ; Machine Learning ; Support Vector Machine ; Blood Cell Count

This study used machine learning techniques combined with routine blood cell analysis parameters to build preliminary prediction models, helping differentiate patients with depression from healthy controls, or patients with anxiety. A multicenter study was performed by collecting blood cell analysis data of Beijing Chaoyang Hospital and the First Hospital of Hebei Medical University from 2020 to 2021. Machine learning techniques, including support vector machine, decision tree, naïve Bayes, random forest and multi-layer perceptron were explored to establish a prediction model of depression. The results showed that based on the blood cell analysis results of healthy controls and depression group, the accuracy of prediction model reached as high as 0.99, F1 was 0.975. Receiver operating characteristic curve area and average accuracy were 0.985 and 0.967, respectively. Platelet parameters contributed mostly to depression prediction model. While, to random forest differential diagnosis model based on the data from depression and anxiety groups, prediction accuracy reached 0.68 and AUC 0.622. Age, platelet parameters, and average volume of red blood cells contributed the most to the model. In conclusion, the study researched on the prediction model of depression by exploring blood cell analysis parameters, revealing that machine learning models were more objective in the evaluation of mental illness.
Humans ; Depression ; Bayes Theorem ; Machine Learning ; Support Vector Machine ; Blood Cell Count

Objective: To establish a survival prediction model based on the independent prognostic factors of long-term prognosis after laparoscopic liver resection(LLR) for intrahepatic cholangiocarcinoma(ICC). Methods: The clinical and pathological data of 351 consecutive patients with ICC who received radical LLR in 13 Chinese medical centers from August 2010 to May 2021 were collected retrospectively. There were 190 males and 161 females,aged(M(IQR)) 61(14)years(range:23 to 93 years). The total cohort was randomly divided into a training dataset(264 cases) and a validation dataset(87 cases). The patients were followed up by outpatient service or telephone,and the deadline for follow-up was October 2021. Based on the training dataset,the multivariate Cox proportional hazards regression model was used to screen the independent influencing factors of long-term prognosis to construct a Nomogram model. The Nomogram model's discrimination,calibration,and clinical benefit were evaluated through internal and external validation,and an assessment of the overall value of two groups was made through the use of a receiver operating characteristic(ROC) curve. Results: There was no significant difference in clinical and pathological characteristics and long-term survival results between the training and validation datasets(all P>0.05). The multivariate Cox analysis showed that CA19-9,CA125,conversion to laparotomy during laparoscopic surgery,and lymph node metastasis were independent prognostic factors for ICC patients after LLR(all P<0.05). The survival Nomogram was established based on the independent prognostic factors obtained from the above screening. The ROC curve showed that the area under the curve of 1, 3 and 5-year overall survival rates of patients in the training dataset were 0.794(95%CI:0.721 to 0.867),0.728(95%CI:0.618 to 0.839) and 0.799(95%CI:0.670 to 0.928),and those in the validation dataset were 0.787(95%CI:0.660 to 0.915),0.831(95%CI:0.678 to 0.983) and 0.810(95%CI:0.639 to 0.982). Internal and external validation proved that the model exhibited a certain discrimination,calibration,and clinical applicability. Conclusion: The survival Nomogram model based on the independent influencing factors of long-term prognosis after LLR for ICC(including CA19-9,CA125,conversion to laparotomy during laparoscopic surgery,and lymph node metastasis) exhibites a certain differentiation,calibration,and clinical practicability.
Bile Duct Neoplasms/surgery* ; Bile Ducts, Intrahepatic/pathology* ; CA-19-9 Antigen ; Cholangiocarcinoma/diagnosis* ; Female ; Humans ; Laparoscopy ; Lymphatic Metastasis ; Male ; Nomograms ; Prognosis ; Retrospective Studies

Pruning branches and leaves is the measure to stimulate the growth of Lonicera japonica flower buds, and consequently, the resources of pruned leaves are inevitably and seriously wasted in production. High-performance liquid chromatography(HPLC) was applied for content determination of seven active ingredients(chlorogenic acid, galuteolin, isochlorogenic acids A, B, and C, secologanic acid, and secoxyloganin) in L. japonica leaves from March to November. The results showed that the tillering removed from the trunk of L. japonica in March, the leaves pruned from May to July, and the leaves after the first frost date in November were rich in active ingredients, which deserved further exploitation and utilization. The total content(TC) of active ingredients in pruned L. japonica leaves in early March was the highest. The content of active ingredients in L. japonica leaves increased significantly after the first frost date, which was close to that in the bud tillers pruned in early and middle March. After the first frost date, L. japonica leaves are incapable of photosynthesis, and the harvesting of L. japonica leaves does not affect the physiological activities of the tree. In addition to huge resources, the content of active ingredients is high during this period, which is the best harvesting period of L. japonica leaves.
Chromatography, High Pressure Liquid/methods* ; Flowers ; Lonicera ; Plant Leaves