Background: To investigate directional deviations between pilot holes and final pedicle screw trajectories in freehand placement and analyze risk factors for misplacement. While pedicle screws are widely used in thoracolumbosacral surgery, directional deviations between pilot holes and final screw trajectories remain understudied as potential risk factors for misplacement. Methods: Thirty-three patients undergoing posterior fixation were prospectively enrolled. Inertial measurement units (IMUs) tracked the spatial positions of pedicle finders and screwdrivers via custom software. Surgeons (n = 6) were stratified into senior, middle, and junior groups. Analyzed variables included the use of tapping, surgeon experience, spinal deformity status, screw tip morphology, and surgical parameters. Results: Among 198 implanted screws (6.00 ± 2.88/patient), all exhibited trajectory deviations (8.12° ± 3.47°). Tapping significantly reduced deviations (7.23° ± 3.23° vs. 8.87° ± 3.31°, p < 0.01). Senior surgeons achieved smaller deviations (7.34° ± 3.33°) than the middle group (8.60° ± 3.51°, p = 0.039) and junior group (8.68° ± 3.49°, p = 0.022). Multivariate analysis confirmed tapping (p = 0.001) and senior experience (p = 0.023) as protective factors. No significant associations emerged for spinal deformity (8.43° ± 3.73° vs. 7.97° ± 3.35°, p = 0.377), screw tip type (cylindrical, 8.43° ± 3.26° vs. tapered, 8.07 ± 3.51°; p = 0.637), obesity, or surgical position parameters. Conclusions Freehand pedicle screw placement consistently produces trajectory deviations from pilot holes, and surgeon experience and tapping are modifiable protective factors. The IMU-based tracking system enables quantitative surgical motion analysis, suggesting its utility for training optimization and technique standardization.

To formulate an expert consensus on the principles of preoperative hair removal and provide scientific guidance for standardized removal of hair before surgical procedures so as to reduce the incidence of surgical site infections.METHODS Led by the Hospital Management Institute of National Health Commission of the People's Republic of China,this consensus was reached with the joint efforts from the expects of relevant fields such as surgeries,interventional therapies,nursing,and infection prevention and control.The consensus facilitates the classification and evaluation of literatures by following the evidence grade formulated by Oxford Evidence-based Medicine Center and focuses on the association of preoperative hair removal with surgical site infection,it reaches the evidence grade of expert consensus and recommendation intensity by integrating with discussions on meetings and clinical experience of the expects from relevant fields.RESULTS A total of 6 items of consensus were reached by summarizing the latest evidence on the aspects including the indications for preoperative hair removal,tools,range,timing and places.CONCLUSION The consensus,to some extent,make supplements to and complete the exiting regulations and standards.It provides guidance for the medical institutions to carry out the preoperative hair removal.

OBJECTIVE To observe the effects of silicone oil removal detergent and multi-enzyme detergent on re-moving the residual silicone oil from digestive endoscopes so as to provide bases for clinical cleaning of digestive endoscopes.METHODS Totally 60 colonoscopes that were used in the endoscopy center of Shenzhen Hospital of Southern Medical University in Jul.2024 were chosen as the research subjects and were randomly divided in to the experimental group with 30 colonoscopes(the silicone-removing multi-enzyme detergent group)and the control group with 30 colonoscopes(the multi-enzyme detergent group),the two groups were respectively treated with sil-icone-removing multi-enzyme detergent and multi-enzyme detergent during the cleaning process.The average bac-terial colony counts of rinsed endoscopes,residual water droplet counts of the cleaned endoscopes and absorbance values of residual methyl silicone oil in the biopsy forceps channels of the endoscopes were observed and compared between the two groups so as to evaluate the effect on removing silicone oil.RESULTS The median average bacteri-al colony counts were 3.05(1.62,4.68)× 103 CFU/piece in the experimental group,36.70(34.66,38.10)× 103 CFU/piece in the control group(Z=-6.654,P＜0.001).The median counts of residual water droplets on the en-doscopes were 7.00(5.00,16.50)in the experimental group,31.50(12.00,43.00)in the control group(Z=-3.940,P＜0.001).The median absorbance value of residual methyl silicone oil in the biopsy forceps chan-nels of the endoscopes was 6.85(5.58,7.85)× 10-3 in the experimental group,36.50(35.50,43.65)× 10-3 in the control group(Z=-6.655,P＜0.001).CONCLUSION The silicone-removing multi-enzyme detergent shows more remarkable effect on removing the residual silicone oil on the endoscopes than the multi-enzyme detergent,it improves the cleaning effect of the endoscopes and is worthy to be verified and promoted in the hospital.

Objective This study aimed to investigate the antimicrobial resistance profiles of bacterial strains isolated from pediatric intensive care units(PICU)in China for better antimicrobial therapy.Methods Clinical isolates were collected from 17 institutions,including tertiary care children's hospitals and pediatric department of tertiary general hospitals in China from January 1,2020 to December 31,2022.Antimicrobial susceptibility testing was carried out according to a unified protocol using Kirby-Bauer method or automated systems.Results were interpreted according to the breakpoints released by the Clinical and Laboratory Standards Institute(CLSI)in 2020.Results A total of 10 688 isolates were collected,including gram-positive organisms(39.2％)and gram-negative organisms(60.8％).The top three organisms were S.aureus(13.6％,1 453/10 688),A.baumannii(10.0％,1 067/10 688),and coagulase-negative Staphylococcus(9.9％,1 058/10 688).Multi-drug resistant organisms(MDROs)were very common in children.The prevalence of methicillin-resistant Staphylococcus aureus(MRSA),carbapenem-resistant Enterobacterales(CRE),carbapenem-resistant E.coli,carbapenem-resistant K.pneumoniae(CRKP),carbapenem-resistant A.baumannii(CRAB),and carbapenem-resistant P.aeruginosa(CRPA)was 41.1％,19.4％,8.8％,30.9％,67.4％,and 28.8％,respectively.Overall,more than 50％of Enterobacteriales isolates were resistant to cephalosporins,while nearly 25％of Enterobacteriales isolates were resistant to carbapenems.MDROs were highly resistant to commonly used antibiotics.More than 80％of CRE and CRAB strains were resistant to all beta-lactam antibiotics.CRE and CRAB showed low resistance rates to tigecycline and polymyxin.CRPA showed lower resistance rates to piperacillin,beta-lactamase inhibitor combinations than the resistance rates to third and fourth generation cephalosporins.All of the Staphylococcus and Enterococcus isolates were susceptible to vancomycin and tigecycline.None of PRSP strains isolated from meningitis and nonmeningitis samples were resistant to rifampicin,vancomycin,or linezolid.The prevalence of β-lactamase-negative ampicillin-resistant(BLNAR)strains was 43.3％in Haemophilus influenzae.Conclusions MDROs were prevalent in PICU.It is necessary to establish an effective multidisciplinary team(MDT)to control the antimicrobial resistance.

Objective:To evaluate the clinical diagnosis and laparoscopic surgical management of diaphragmatic rupture.Methods:Clinical data of 19 patients diagnosed with diaphragmatic hernia secondary to diaphragmatic rupture, admitted to the Sixth Affiliated Hospital of Sun Yat-sen University from Jan 2022 to Sep 2024 was retrospectively analyzed.Results:There were 7 males and 12 females. Traumatic etiology was confirmed in 9 cases, all developed after closed injuries, including one vehicular accident, seven blunt traumas, and one fall from height. Iatrogenic factors accounted for the remaining 10 cases. Left-sided diaphragmatic hernias were observed in 13 patients, while right-sided hernias occurred in 6 patients. Hiatal hernias were complicated in 5 patients. All patients underwent elective laparoscopic tension-free repair using composite patch for defect closure. Postoperatively, one patient was readmitted into ICU due to reexpansion pulmonary edema but was subsequently discharged following recovery. The median postoperative hospital stay was 10 days.Conclusions:The incidence of diaphragmatic rupture is relatively uncommon, primarily occurring as a result of traumatic or iatrogenic injuries. Early diagnosis coupled with prompt laparoscopic surgical intervention has proven to be effective in managing this condition.

The incidence rate of adult diaphragmatic hernia is low, which can be divided into congenital diaphragmatic hernia and acquired diaphragmatic hernia. In emergency situations, it may present as incarceration or strangulation. In non-emergency situations, it may be asymptomatic or present with nonspecific respiratory and digestive symptoms, making it prone to misdiagnosis and missed diagnosis. For suspected cases, multi-slice spiral CT examination should be performed, with attention paid to sagittal imaging. In emergency situations, CT contrast-enhanced scanning should be focusing on assessing the viability of the hernia contents. Once diagnosed, surgical treatment should be performed, with transabdominal approach being the preferred choice. In emergency situations, laparoscopic exploration and repair can be considered for patients with stable hemodynamics. For delayed traumatic diaphragmatic hernia and adult congenital diaphragmatic hernia with large defects, the necessity of a thoracic approach should be considered. During surgery, the complete removal of the hernia sac should not be pursued. The decision to remove or partially remove the hernia sac should be evaluated based on intraoperative conditions. Intraoperative diaphragmatic defects should be closed as much as possible with non-absorbable sutures. For diaphragmatic defects that are tensioned after suturing, or those close to the costal margin, anti-adhesion mesh should be used by intraperitoneal repair approach. When fixing the patch near the pericardium, the use of tacks should be avoided.

Objective:To establish and validate a multimodal model based on radiomics and deep learning for predicting acute pancreatitis (AP) complicated with acute respiratory distress syndrome (ARDS).Methods:Patients diagnosed with AP from The First Affiliated Hospital of Soochow University, Donghai County People's Hospital and Jintan Affiliated Hospital of Jiangsu University between January 2017 and December 2023 were enrolled. Based on the diagnosis of ARDS within 1 week after admission, the patients were classified into the ARDS group and the non-ARDS group. Patients in the First Affiliated Hospital of Soochow University ( n=406) was used as the training set (non-ARDS group n=212 vs ARDS group n=194), while Donghai and Jintan hospitals served as the test set ( n=175; non-ARDS group n=104 vs ARDS group n=71). Clinical data, laboratory tests and the occurrence of systemic inflammatory response syndrome (SIRS) within 24 hours after admission were collected. Scoring systems such as bedside index for severity in acute pancreatitis (BISAP), Ranson score and modified CT severity index (MCTSI) were calculated. Radiomics features were extracted from three-dimensional CT images to develop a radiomics model based on XGBoost algorithm. At the same time, a deep learning model was constructed using deep convolutional networks to extract deep features. Finally, clinical features and the predictions from the aforementioned models were integrated to establish a multimodal model based on XGBoost algorithm. To enhance model visualization, variable importance ranking and local interpretable visualization were used. The receiver operating characteristic (ROC) curves of the three models and the three scores including BISAP, Ranson and MCTSI were plotted and the area under the curves (AUCs) were calculated to evaluate the prediction performance for ARDS in AP patients, as well as sensitivity and specificity. Results:In the multimodal model for predicting ARDS in AP patients, predictions of the deep learning model and the radiomics model were the most important variables, followed by SIRS, C-reactive protein, procalcitonin, albumin, glucose, creatinine, neutrophil, and Ca 2+. In the training set, the multimodal model achieved an AUC of 0.933 for predicting ARDS in AP patients, higher than the radiomics model (0.727), the deep learning model (0.877), MCTSI (0.870), Ranson (0.620) and BISAP (0.898). In the test set, the model's AUC was 0.916 for predicting ARDS in AP patients, higher than the radiomics model (0.660), the deep learning model (0.864), MCTSI (0.851), Ranson (0.609), and BISAP (0.860). Conclusions:Based on clinical structured data, radiomics and deep learning features, the multimodal model could predict the risk of ARDS in AP patients at an early stage, whose performance is better than the single-modal models and the traditional scoring systems.

Objective To explore the application effect of the"ASK-T"aspiration prevention management model in reducing the risk of aspiration in elderly patients with chronic obstructive pulmonary disease(COPD)complicated by dysphagia.Methods 196 of 409 COPD patients with aspiration risk admitted to the Respiratory Disease Department of Guangdong Provincial Hospital of Chinese Medicine,Higher Education Mega Center Hospital from July 2021 to June 2024 were selected as the study object,and 102 COPD patients with aspiration risk admitted from July 2021 to December 2022 were set as the pre-improvement group.The patients in this group received the original nursing process and management mode of the department during hospitalization.94 COPD patients with aspiration risk admitted from January 2023 to June 2024 were set as the improved group,and the patients in this group were introduced into the department and implemented an innovative improvement project:"ASK-T"anti-aspiration management mode and then received nursing management.Patients in both groups were observed and followed up for 4 weeks.The risk grade of aspiration,SSA score,DHI score,aspiration pneumonia and aspiration pneumonia,nutritional status and satisfaction after intervention were compared between the two groups.Result in the improved group was significantly lower than that in the pre-improvement group(P<0.05),and the SSA score,DHI score and total DHI score of patients in the 2 groups were significantly lower than that before intervention(P<0.05).The SSA score,DHI score and DHI total score in the improved group were lower than those in the pre-improved group(P<0.05).The results of 4-week follow-up showed that the scores of NRS-2002 for nutritional risk screening in 2 groups were lower than before intervention(P<0.05),and the scores of NRS-2002 for nutritional risk screening in the improved group were significantly lower than those in the pre-improvement group(P<0.05).The comparison showed that the incidence of aspiration and aspiration pneumonia in the improved group was lower than that in the pre-improved group(P<0.05),and the satisfaction score of patients with the relevant preventive measures was higher than that in the pre-improved group(P<0.05).Conclusion The"ASK-T"aspiration preven-tion management model,when applied in the nursing of elderly patients with COPD complicated by dysphagia,can significantly reduce the risk of aspiration and the incidence of aspiration pneumonia,effectively improve patients' swallowing function and quality of life,and enhance their nutritional status.Furthermore,this model significantly improves patient satisfaction with intervention measures,providing a scientific and effective management strategy for clinical nursing of elderly patients with COPD complicated by dysphagia.It is worthy of promotion and application in clinical practice.

Objective:To establish and validate a multimodal model based on radiomics and deep learning for predicting acute pancreatitis (AP) complicated with acute respiratory distress syndrome (ARDS).Methods:Patients diagnosed with AP from The First Affiliated Hospital of Soochow University, Donghai County People's Hospital and Jintan Affiliated Hospital of Jiangsu University between January 2017 and December 2023 were enrolled. Based on the diagnosis of ARDS within 1 week after admission, the patients were classified into the ARDS group and the non-ARDS group. Patients in the First Affiliated Hospital of Soochow University ( n=406) was used as the training set (non-ARDS group n=212 vs ARDS group n=194), while Donghai and Jintan hospitals served as the test set ( n=175; non-ARDS group n=104 vs ARDS group n=71). Clinical data, laboratory tests and the occurrence of systemic inflammatory response syndrome (SIRS) within 24 hours after admission were collected. Scoring systems such as bedside index for severity in acute pancreatitis (BISAP), Ranson score and modified CT severity index (MCTSI) were calculated. Radiomics features were extracted from three-dimensional CT images to develop a radiomics model based on XGBoost algorithm. At the same time, a deep learning model was constructed using deep convolutional networks to extract deep features. Finally, clinical features and the predictions from the aforementioned models were integrated to establish a multimodal model based on XGBoost algorithm. To enhance model visualization, variable importance ranking and local interpretable visualization were used. The receiver operating characteristic (ROC) curves of the three models and the three scores including BISAP, Ranson and MCTSI were plotted and the area under the curves (AUCs) were calculated to evaluate the prediction performance for ARDS in AP patients, as well as sensitivity and specificity. Results:In the multimodal model for predicting ARDS in AP patients, predictions of the deep learning model and the radiomics model were the most important variables, followed by SIRS, C-reactive protein, procalcitonin, albumin, glucose, creatinine, neutrophil, and Ca 2+. In the training set, the multimodal model achieved an AUC of 0.933 for predicting ARDS in AP patients, higher than the radiomics model (0.727), the deep learning model (0.877), MCTSI (0.870), Ranson (0.620) and BISAP (0.898). In the test set, the model's AUC was 0.916 for predicting ARDS in AP patients, higher than the radiomics model (0.660), the deep learning model (0.864), MCTSI (0.851), Ranson (0.609), and BISAP (0.860). Conclusions:Based on clinical structured data, radiomics and deep learning features, the multimodal model could predict the risk of ARDS in AP patients at an early stage, whose performance is better than the single-modal models and the traditional scoring systems.

Chinorhodopyr is a widely used pesticide in agriculture, and the mortality rate of acute chinorhodopyr poisoning patients is high, with no effective treatment currently available. This paper summarized the clinical data of 8 patients with acute chlorfenapyr poisoning recently admitted to the First Affiliated Hospital of Wenzhou Medical University, including clinical manifestations, auxiliary examination and treatment process, so as to provide references for disease diagnosis and treatment, and improve clinicians' understanding of acute chlorfenapyr poisoning.