OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

OBJECTIVE: To compare the efficacy and safety of intravenous colistin sulfate combined with nebulized inhalation versus intravenous monotherapy for pulmonary infections caused by carbapenem-resistant organism (CRO). METHODS: A multicenter retrospective cohort study was conducted. Clinical data were collected from patients admitted to the intensive care unit (ICU) of 10 tertiary class-A hospitals in Henan Province between July 2021 and May 2023, who received colistin sulfate for CRO pulmonary infections. Data included baseline characteristics, inflammatory markers [white blood cell count (WBC), neutrophil count (NEU), procalcitonin (PCT), C-reactive protein (CRP)], renal function indicators [serum creatinine (SCr), blood urea nitrogen (BUN)], life support measures, anti-infection regimens, clinical efficacy, microbiological clearance rate, and prognostic outcomes. Patients were divided into two groups: intravenous group (colistin sulfate monotherapy via intravenous infusion) and combination group ((intravenous infusion combined with nebulized inhalation of colistin sulfate). Changes in parameters before and after treatment were analyzed. RESULTS: A total of 137 patients with CRO pulmonary infections were enrolled, including 89 in the intravenous group and 48 in the combination group. Baseline characteristics, life support measures, daily colistin dose, and combination regimens (most commonly colistin sulfate plus carbapenems in both groups) showed no significant differences between two groups. The combination group exhibited higher clinical efficacy [77.1% (37/48) vs. 59.6% (52/89)] and microbiological clearance rate [60.4% (29/48) vs. 39.3% (35/89)], both P < 0.05. Pre-treatment inflammatory and renal parameters showed no significant differences between two groups. Post-treatment, the combination group showed significantly lower WBC and CRP [WBC (×10⁹/L): 8.2±0.5 vs. 10.9±0.6, CRP (mg/L): 14.0 (5.7, 26.6) vs. 52.1 (24.4, 109.6), both P < 0.05], whereas NEU, PCT, SCr, and BUN levels showed no significant between two groups. ICU length of stay was shorter in the combination group [days: 16 (10, 25) vs. 21 (14, 29), P < 0.05], although mechanical ventilation duration and total hospitalization showed no significant differences between two groups. CONCLUSIONS Intravenous colistin sulfate combined with nebulized inhalation improved clinical efficacy and microbiological clearance in CRO pulmonary infections with an acceptable safety profile.
Humans ; Colistin/therapeutic use* ; Retrospective Studies ; Administration, Inhalation ; Anti-Bacterial Agents/therapeutic use* ; Carbapenems/pharmacology* ; Male ; Female ; Middle Aged ; Gram-Negative Bacteria/drug effects* ; Aged ; Treatment Outcome ; Respiratory Tract Infections/drug therapy*

Osteoporosis is a prevalent skeletal condition characterized by reduced bone mass and strength, leading to increased fragility. Buqi-Tongluo (BQTL) decoction, a traditional Chinese medicine (TCM) prescription, has yet to be fully evaluated for its potential in treating bone diseases such as osteoporosis. To investigate the mechanism by which BQTL decoction inhibits osteoclast differentiation in vitro and validate these findings through in vivo experiments. We employed MTS assays to assess the potential proliferative or toxic effects of BQTL on bone marrow macrophages (BMMs) at various concentrations. TRAcP experiments were conducted to examine BQTL's impact on osteoclast differentiation. RT-PCR and Western blot analyses were utilized to evaluate the relative expression levels of osteoclast-specific genes and proteins under BQTL stimulation. Finally, in vivo experiments were performed using an osteoporosis model to further validate the in vitro findings. This study revealed that BQTL suppressed receptor activator of NF-κB ligand (RANKL)-induced osteoclastogenesis and osteoclast resorption activity in vitro in a dose-dependent manner without observable cytotoxicity. The inhibitory effects of BQTL on osteoclast formation and function were attributed to the downregulation of NFATc1 and c-fos activity, primarily through attenuation of the MAPK, NF-κB, and Calcineurin signaling pathways. BQTL's inhibitory capacity was further examined in vivo using an ovariectomized (OVX) rat model, demonstrating a strong protective effect against bone loss. BQTL may serve as an effective therapeutic TCM for the treatment of postmenopausal osteoporosis and the alleviation of bone loss induced by estrogen deficiency and related conditions.
Animals ; NFATC Transcription Factors/genetics* ; Drugs, Chinese Herbal/pharmacology* ; Ovariectomy ; Osteoclasts/metabolism* ; Female ; Osteogenesis/drug effects* ; Rats, Sprague-Dawley ; Rats ; NF-kappa B/genetics* ; Osteoporosis/genetics* ; Signal Transduction/drug effects* ; Bone Resorption/genetics* ; Cell Differentiation/drug effects* ; Humans ; RANK Ligand/metabolism* ; Mitogen-Activated Protein Kinases/genetics* ; Transcription Factors

Dental treatments for children and adolescents have unique clinical characteristics that differ from dental care for adults in terms of children's physiology, psychology, and behavior. These differences impose specific requirements on the application of local anesthesia in pediatric dental procedures. This article presents expert consensus on the principles of local anesthesia techniques in pediatric dental therapies, including the use of common anesthetic drugs and dosage control, safety and efficacy evaluation, and prevention and management of complications. The aim is to improve the safety and quality of pediatric dental treatments and offer guidance for clinical application by dentists.
Humans ; Child ; Anesthesia, Local/methods* ; Consensus ; Anesthesia, Dental/methods* ; Adolescent ; Anesthetics, Local/administration & dosage* ; Dental Care for Children

Objective To analyze the changes in optical coherence tomography angiography (OCTA) parameters in patients with essential hypertension，and to explore the effect of blood pressure on OCTA parameters. Methods A total of 164 patients with essential hypertension were selected and divided into controlled blood pressure group (n=92) and uncontrolled blood pressure group (n=72). OCTA examination was performed on the optic disc and macula of all patients, and the right eyes were selected for analysis. Results There were no significant differences in retinal nerve fiber layer (RNFL) thickness, radial peripapillary capillary (RPC) total vascular density, RPC total small vessel density, perifovea superficial capillary plexus (SCP) vascular density, and perifovea deep capillary plexus (DCP) vascular density between the two groups of patients. There were no significant differences in foveal avascular zone (FAZ) area, FAZ diameter, and fovea retinal thickness between the two groups of patients. The density of the parafovea SCP, parafovea DCP, and fractal dimension (FD) in the uncontrolled blood pressure group were significantly lower than those in the controlled blood pressure group (P＜0.05). Multiple linear regression analysis showed that elevation of blood pressure was a independently related factor of reduced parafovea DCP density (P＝0.026), while there was no correlation between the uncontrolled blood pressure and parafovea SCP density and FD level. Conclusions The blood pressure level is correlated with the parafovea DCP density, while has no correlation with other OCTA parameters in hypertension patients.

In the strategic context of "healthy China"， the concept of "medicine and food homology"， rooted in the culture of traditional Chinese medicine （TCM）， has received unprecedented attention. In response to population aging in China， the health of the elderly has become the focus of public health attention， and proactive health is the key to healthy aging. From the perspective of the application of medicinal and edible materials in proactive health and technological responses to population aging for the first time， this paper firstly provided a systematic overview of medicinal and edible materials and the policies related to proactive health. Second， it summarized the situation of modern technology that accelerates the research and development of medicinal and edible products， as well as the current situation of various modern biotechnologies that reveal the mechanism of action of medicinal and edible materials. Third， it discussed the application scenarios of medicinal and edible materials in proactive health and technological responses to population aging， as well as the future research and development of medicinal and edible materials. By exploring in depth the unique value and importance of medicinal and edible materials， the paper lays a theoretical foundation for improving the health care capabilities of TCM and contributes new strategies derived from TCM to healthy aging.

This study was analyzed the spatial-temporal distribution and aggregation characteristics of Yersinia pestispositive host animals and vector pathogens in marmot natural foci in the Qilian-Altun mountains,Gansu Province,to provide a scientific basis for precise plague prevention and control.Y.pestissurveillance data for marmot natural foci in Qilian-Altun Mountains of Gansu Province from 2014 to 2023 were obtained from the Disease Control and Prevention Center of Gansu Province.Origin 2024 software was used for data visualization and presentation.Global and local spatial autocorrelation analyses and trend analyses were conducted in ArcGIS 10.8 software,with townships as the spatial scale.Cumulatively,440 strains of Y.pestis were isolated from the natural marmot foci in the Qilian-Altun mountainsof Gansu Province from 2014 to 2023.Most strains was isolated from marmots(345 strains,78.41%),and the remainder were isolated from vectors.Temporal distribution analysis indicated that the highest number of detected bacteria was reported in July and August(both 121 strains,27.50%).Regional distribution analysis revealed that Aksai County reported the highest number of detected bacteria(255 strains,57.95%).Global spatial autocorrelation analysis showed a spatially clustered distribution of the number of bacteria detected annually in the townships containing natural foci,except in2014,2016,and 2021-2023.The strongest spatial clustering was observed in 2020(Moran's I=0.521 2,Z=14.397 0,P<0.001).Local spatial autocorrelation analysis indicated a"high-high"aggregation area in the natural foci every year from 2014 to 2023,primarily in Hongliuwan Town of Aksai County and Dangchengwan Town of Subei County.The distribution of the"low-low"aggregation area was essentially consistent with the low activity area of the Yersinia pestisepidemic.The trend in annual total bacterial count gradually increased from east to west,and peaked in the western part of the epidemic focus.Clear spatial aggregation characteristics of the number of Y.pestis were detected in the marmot natural foci in the Qilian-Altun mountains at the townshiplevel as a whole in Gansu Province from 2014 to 2023.The aggregation area was mainly in the western section of Qilian Mountain to the Altun mountain section of the epidemic source area.Monitoring and prevention and control efforts should be focused in this key area,with prevention and control measures tailored to the local conditions,and classified guidance to decrease the risk of plague occurrence and spread.

Objective To systematically review the safety of budesonide inhalation aerosol in the treatment of chronic obstructive pulmonary disease.Methods PubMed,Cochrane Library,Web of Science,Embase,CNKI,WanFang Data,VIP and SinoMed databases were electronically searched to collect randomized controlled trials on budesonide inhalation aerosol in the treatment of chronic obstructive pulmonary disease from inception to June 30,2024.Two reviewers independently screened literature,extracted data,and assessed the risk of bias of included studies.The Meta-analysis was performed by using RevMan 5.3 software.Results A total of 52 studies with 16,768 patients were included.Meta-analysis results showed that the incidence of respiratory system adverse drug reaction(ADR)was higher in the budesonide group than in the control group,with a statistically significant difference[OR=1.18,95％CI(1.10,1.27),P＜0.001].There was no statistically significant difference in the incidence of overall ADR,central and peripheral nervous system ADR,gastrointestinal system ADR,skin ADR,urinary system ADR,infectious disease ADR,and musculoskeletal system ADR between the two groups of patients(P＞0.05).According to the subgroup analysis of the treatment course,in the 3-6 month subgroup,the incidence of ADR was higher in the budesonide group than in the control group,with a statistically significant difference[OR=1.43,95％CI(1.18,1.74),P＜0.001].In the studies with a treatment duration of more than 6 months,the incidence of ADR was higher in the budesonide group than in the control group,with a statistically significant difference[OR=1.54,95％CI(1.16,2.03),P=0.002].In the study of unknown treatment course,the incidence of ADR in the budesonide group was lower than that in the control group,and the difference was statistically significant[OR=0.39,95％CI(0.20,0.76),P=0.005].In the studies with a treatment duration of less than 3 months,the incidence of ADR in the budesonidegroup was comparable to that in the control group(P＞0.05).Conclusion The incidence of respiratory ADR caused by budesonide is higher than that of the control group.Although the incidence of overall ADR is not statistically significant,the occurrence of systemic adverse reactions should continue to be closely monitored in the future treatment of chronic obstructive pulmonary disease.

Objective:To investigate the feasibility of flight fatigue being detected via photoplethysmography (PPG) and regional cerebral oxygen saturation (rScO 2) in order to address the challenges posed by flight fatigue during prolonged or multiple consecutive flights. Methods:A total of 16 healthy male volunteers were enrolled. A wireless cerebral oximetry monitor headband was employed to collect PPG and rScO 2 data from the forehead while a multi-lead physiological data acquisition system was used concurrently to record three-lead electrocardiograms (ECGs). After 18 h of sleep deprivation, each volunteer performed a flight-simulating task, which was divided into 4 stages: the baseline period (T1), relaxation period (T2), early fatigue period (T3) and severe fatigue period (T4). Five-minute data was collected from each stage for analysis using AcqKnowledge 6.0. Heart rate (HR) and 3 HR variability (HRV) metrics, namely standard deviation of NN intervals (SDNN), root mean square of successive differences (RMSSD), and low frequency to high frequency power ratio (LF/HF), were computed independently from both ECG and PPG traces. The mean rScO 2 value for each stage was used to represent the cerebral oxygen saturation during that stage. The intra-class correlation coefficient (ICC) was employed to assess the consistency of the measurements, and the differences in HR and HRV indicators of the volunteers in the 4 stages of the experiment were analyzed. Results:The HR measured by ECG and PPG was highly consistent across the 4 stages among the 14 volunteers ( ICC=0.951, 0.963, 0.962, 0.963, P=0.013, 0.011, 0.021, 0.015), so were SDNN, RMSSD and LF/HF values ( ICC=0.935-0.983, all P<0.05). HR values calculated with either method showed significant differences across the 4 stages in the 14 volunteers ( F=21.63, 20.52, P=0.007, 0.008). HR gradually declined from T1 to T4, and was significantly lower at T4 than at T1 ( P=0.011, 0.009). There were significant differences in SDNN ( F=22.31, 24.26, P=0.006, 0.003), RMSSD ( F=22.30, 22.26, P=0.006, 0.006), and LF/HF ( F=20.37, 25.13, P=0.009, 0.002) across the 4 stages among the 14 volunteers. SDNN and RMSSD kept increasing as fatigue was intensified, while LF/HF decreased correspondingly. Statistically significant differences were found in SDNN, RMSSD and LF/HF values between T4 and T1 (all P<0.01). rScO 2 measured during the flight-simulating trial also differed significantly across the 4 stages ( F=21.39, P=0.007). rScO? at both T3 and T4 was significantly lower than at T1 ( P=0.009, 0.007). Conclusions:PPG can replace ECG for monitoring HR and HRV indicators under flight fatigue. Furthermore, the combination of PPG with rScO 2 monitoring allows for earlier detection of flight fatigue. This study is expected to offer a user-friendly and non-invasive approach to management of pilot fatigue.