The incidence and mortality rates of hepatocellular carcinoma(HCC)remain high in China,and the application of surgical resection is often limited due to the fact that most patients are in the advanced stage at the time of confirmed diagnosis.This article reviews commonly used advanced locoregional therapies for HCC and the advances in mainstream techniques such as local ablation(radiofrequency ablation,microwave ablation,irreversible electroporation,and cryoablation),intravascular intervention(transcatheter arterial chemoembolization,hepatic arterial infusion chemotherapy,and Y90 hepatic arterial infusion chemotherapy),and radiotherapy(CyberKnife,proton therapy,and heavy-ion therapy),and a multidimensional decision-making framework is constructed for HCC locoregional therapy by comparing treatment principles,indications,limitations,and clinical data of these techniques.This article aims to provide evidence-based support for persistent dilemmas in clinical decision-making,promote the role of locoregional therapies in clinical practice,and propose the directions for future research and clinical application.This article also establishes a comprehensive clinical roadmap for HCC locoregional therapy,which helps to address current challenges regarding technique selection and delineate future directions for innovation,in order to reshape the treatment of HCC through technological integration and paradigm innovation.

Objective:To construct a predictive model for mortality in patients with multiple trauma combined with thoracic injuries and evaluate its predictive value.Methods:A retrospective cohort study was conducted to analyze the clinical data of 184 patients with multiple trauma combined with thoracic injuries admitted to the International Zhuang Medicine Hospital Affiliated to Guangxi University of Chinese Medicine from April 2019 to December 2023, including 129 males and 55 females, aged 19-85 years [(46.1±13.7)years]. According to the prognostic outcomes at 3-month follow-up after discharge, the patients were divided into survival group ( n=145) and death group ( n=39). Data were recorded in both groups at admission, including gender, age, and cause of injury, laboratory tests such as systolic blood pressure, oxygen saturation (SaO 2), hemoglobin (Hb), neutrophil-to-lymphocyte ratio (NLR), and lactate, combined injuries such as the number of combined injuries, number of rib fracture, bilateral rib fracture, first-rib fracture, sternum fracture, thoracic vertebral fracture, bilateral pulmonary contusion, bilateral pneumothorax, subarachnoid hemorrhage, subdural hematoma, epidural hematoma, skull fracture, skull base fracture, cervical vertebral fracture, brain herniation, cerebral contusion, lumbar vertebral fracture, pelvic and abdominal cavity hematoma, liver injury, kidney injury, spleen injury, clavicle fracture, scapular fracture, femoral fracture, and pelvic fracture, and injury scores such as shock index (SI), modified shock index (MSI), injury severity score (ISS), revised trauma score (RTS), Glasgow coma score (GCS), and thoracic trauma severity (TTS) score. Univariate binary logistic regression analysis was used to screen for risk factors of death in patients with multiple trauma combined with thoracic injuries. LASSO regression and multivariate logistic regression analysis were employed to identify predictive variables and independent risk factors for mortality in those patients and to construct a regression equation. A nomogram prediction model based on the regression equation was developed using R language. Receiver operating characteristic (ROC) curves were plotted to evaluate the discrimination of the model. The ROC curves were internally validated using the Bootstrap method with 1 000 resamples. The calibration of the model was assessed using the Hosmer-Lemeshow (H-L) goodness-of-fit test. The clinical application value of the model was evaluated using decision curve analysis (DCA) and clinical impact curve (CIC) analysis. Results:There were statistically significant differences between the survival group and the death group in systolic blood pressure, SaO 2, NLR, lactate, number of combined injuries, subarachnoid hemorrhage, subdural hematoma, skull fracture, skull base fracture, brain herniation, liver injury, SI, MSI, ISS, RTS, GCS, and TTS ( P<0.05 or 0.01). The results of the univariate binary logistic regression analysis showed that the above-mentioned related variables except for systolic blood pressure were all significantly associated with death in patients with multiple trauma combined with thoracic injuries ( P<0.05 or 0.01). Five predictive variables, TTS, GCS, brain herniation, ISS, and lactate were obtained in LASSO regression analysis. The results of the multivariate logistic regression analysis showed that GCS ( OR=0.70, 95% CI 0.58, 0.83), brain herniation ( OR=46.18, 95% CI 4.27, 499.26), TTS ( OR=1.71, 95% CI 1.30, 2.24), and lactate ( OR=1.35, 95% CI 1.01, 1.80) were independent risk factors for death in patients with multiple trauma combined with thoracic injuries ( P<0.05 or 0.01). Based on the aforementioned independent risk factors, a regression formula was constructed as follows: P=e x/(1+e x), with the x=-0.36×"GCS"+3.83×"brain herniation"+0.53×"TTS"+0.30×"lactate levels"-11.03. The area under the ROC curve (AUC) of the predictive model for mortality in patients with multiple trauma combined with thoracic injuries based on the equation was 0.97 (95% CI 0.93, 1.00). The AUC was internally validated using the Bootstrap method with 1 000 samples, resulting in an AUC of 0.97 (95% CI 0.91, 1.00). The results of the H-L goodness-of-fit test showed that the bias-corrected calibration curve of the model was in good consistence with the actual curve and both of them were close to the ideal curve. In the evaluation of the clinical application value of the predictive model, the DCA results showed that the predictive model could achieve good clinical net benefit. The CIC results showed that when the threshold probability was greater than 0.7, the model-identified high-risk patients for death highly matched the patients who actually died. Conclusion:The predictive model for mortality in patients with multiple trauma combined with thoracic injuries based on GCS, brain herniation, TTS, and lactate has good predictive performance and clinical application value.

The incidence and mortality rates of hepatocellular carcinoma(HCC)remain high in China,and the application of surgical resection is often limited due to the fact that most patients are in the advanced stage at the time of confirmed diagnosis.This article reviews commonly used advanced locoregional therapies for HCC and the advances in mainstream techniques such as local ablation(radiofrequency ablation,microwave ablation,irreversible electroporation,and cryoablation),intravascular intervention(transcatheter arterial chemoembolization,hepatic arterial infusion chemotherapy,and Y90 hepatic arterial infusion chemotherapy),and radiotherapy(CyberKnife,proton therapy,and heavy-ion therapy),and a multidimensional decision-making framework is constructed for HCC locoregional therapy by comparing treatment principles,indications,limitations,and clinical data of these techniques.This article aims to provide evidence-based support for persistent dilemmas in clinical decision-making,promote the role of locoregional therapies in clinical practice,and propose the directions for future research and clinical application.This article also establishes a comprehensive clinical roadmap for HCC locoregional therapy,which helps to address current challenges regarding technique selection and delineate future directions for innovation,in order to reshape the treatment of HCC through technological integration and paradigm innovation.

Objective:To construct a predictive model for mortality in patients with multiple trauma combined with thoracic injuries and evaluate its predictive value.Methods:A retrospective cohort study was conducted to analyze the clinical data of 184 patients with multiple trauma combined with thoracic injuries admitted to the International Zhuang Medicine Hospital Affiliated to Guangxi University of Chinese Medicine from April 2019 to December 2023, including 129 males and 55 females, aged 19-85 years [(46.1±13.7)years]. According to the prognostic outcomes at 3-month follow-up after discharge, the patients were divided into survival group ( n=145) and death group ( n=39). Data were recorded in both groups at admission, including gender, age, and cause of injury, laboratory tests such as systolic blood pressure, oxygen saturation (SaO 2), hemoglobin (Hb), neutrophil-to-lymphocyte ratio (NLR), and lactate, combined injuries such as the number of combined injuries, number of rib fracture, bilateral rib fracture, first-rib fracture, sternum fracture, thoracic vertebral fracture, bilateral pulmonary contusion, bilateral pneumothorax, subarachnoid hemorrhage, subdural hematoma, epidural hematoma, skull fracture, skull base fracture, cervical vertebral fracture, brain herniation, cerebral contusion, lumbar vertebral fracture, pelvic and abdominal cavity hematoma, liver injury, kidney injury, spleen injury, clavicle fracture, scapular fracture, femoral fracture, and pelvic fracture, and injury scores such as shock index (SI), modified shock index (MSI), injury severity score (ISS), revised trauma score (RTS), Glasgow coma score (GCS), and thoracic trauma severity (TTS) score. Univariate binary logistic regression analysis was used to screen for risk factors of death in patients with multiple trauma combined with thoracic injuries. LASSO regression and multivariate logistic regression analysis were employed to identify predictive variables and independent risk factors for mortality in those patients and to construct a regression equation. A nomogram prediction model based on the regression equation was developed using R language. Receiver operating characteristic (ROC) curves were plotted to evaluate the discrimination of the model. The ROC curves were internally validated using the Bootstrap method with 1 000 resamples. The calibration of the model was assessed using the Hosmer-Lemeshow (H-L) goodness-of-fit test. The clinical application value of the model was evaluated using decision curve analysis (DCA) and clinical impact curve (CIC) analysis. Results:There were statistically significant differences between the survival group and the death group in systolic blood pressure, SaO 2, NLR, lactate, number of combined injuries, subarachnoid hemorrhage, subdural hematoma, skull fracture, skull base fracture, brain herniation, liver injury, SI, MSI, ISS, RTS, GCS, and TTS ( P<0.05 or 0.01). The results of the univariate binary logistic regression analysis showed that the above-mentioned related variables except for systolic blood pressure were all significantly associated with death in patients with multiple trauma combined with thoracic injuries ( P<0.05 or 0.01). Five predictive variables, TTS, GCS, brain herniation, ISS, and lactate were obtained in LASSO regression analysis. The results of the multivariate logistic regression analysis showed that GCS ( OR=0.70, 95% CI 0.58, 0.83), brain herniation ( OR=46.18, 95% CI 4.27, 499.26), TTS ( OR=1.71, 95% CI 1.30, 2.24), and lactate ( OR=1.35, 95% CI 1.01, 1.80) were independent risk factors for death in patients with multiple trauma combined with thoracic injuries ( P<0.05 or 0.01). Based on the aforementioned independent risk factors, a regression formula was constructed as follows: P=e x/(1+e x), with the x=-0.36×"GCS"+3.83×"brain herniation"+0.53×"TTS"+0.30×"lactate levels"-11.03. The area under the ROC curve (AUC) of the predictive model for mortality in patients with multiple trauma combined with thoracic injuries based on the equation was 0.97 (95% CI 0.93, 1.00). The AUC was internally validated using the Bootstrap method with 1 000 samples, resulting in an AUC of 0.97 (95% CI 0.91, 1.00). The results of the H-L goodness-of-fit test showed that the bias-corrected calibration curve of the model was in good consistence with the actual curve and both of them were close to the ideal curve. In the evaluation of the clinical application value of the predictive model, the DCA results showed that the predictive model could achieve good clinical net benefit. The CIC results showed that when the threshold probability was greater than 0.7, the model-identified high-risk patients for death highly matched the patients who actually died. Conclusion:The predictive model for mortality in patients with multiple trauma combined with thoracic injuries based on GCS, brain herniation, TTS, and lactate has good predictive performance and clinical application value.

In order to investigate the effects of porin genes ompW,ompS and ompD on the biological properties and virulence of Salmonella typhimurium,the corresponding mutant strains were con-structed using the λ Red homologous recombination system,and the growth curves,motility,bio-chemical properties,in vitro genetic stability,biofilm-forming ability,drug resistance,and lethal dose at half capacity(LD50)between the standard strain and each mutant strain were detected by comparative assays for Salmonella typhimurium.The results showed that,compared with the standard strain,the ompD and ompW mutation had less effect on the growth rate and motility of the bacteria,while the ompS mutation significantly reduced the growth rate and motility;none of the three genetic mutation affected the biochemical characteristics of Salmonella typhimurium,nor the genetic stability,but affected its susceptibility to a variety of commonly used antibiotics to varying degrees and caused a highly significant decrease(P＜0.01)in the ability to form a biofilm,and the results showed that the three mutant strains had a significant reduction in the ability to form a biofilm.The result of LD50 virulence assay showed that all three genetic mutation led to a decrease in the virulence of Salmonella typhimurium,among which the ompS mutant strain showed the most obvious decrease in virulence,LD50 was 25 times that of the standard strain.In conclusion,mutations of the pore protein ompW,ompS,and ompD genes can affect some biological properties of Salmonella typhimurium.The results of this study laid an experimental foundation for further research on the biological functions of the pore protein ompW,ompS and ompD genes and Salmonella pathogenicity.

In order to establish a specific,rapid and convenient method for the detection of Campy-lobacter jejuni(C.jejuni).A set of specific primers and a probe that do not cause false positives were designed with the hipO gene of C.jejuni as the target,and the 5'ends of the downstream primers and probes were labeled with biotin and fluorescein,respectively.C.jejuni-RPA-LFD had no cross-reactivity with Klebsiella pneumoniae,Escherichia coli,Pseudomonas,Bacillus cereus,Pasteurella,Proteus mirabilis,and Salmonella typhimurium,and the optimal reaction system was 37 ℃,25 min,and its sensitivity could reach 3.93×100 copies/μL,and 1 × 102 CFU/mL of C.jejuni contaminated stool samples could be detected in the simulated detection.The C.jejuni-RPA-LFD established in this study has the advantages of good specificity,simplicity,rapidity and high sensitivity,which provides an effective way for the rapid diagnosis of C.jejuni and the con-trol of the spread of C.jejuni at the grassroots level of livestock and poultry farming.

Objective:To investigate the current status and quality and safety of radiation therapy resources in medical institutions in Hunan province.Methods:The basic situation questionnaire, quality and safety self-assessment form were designed according to the content of the survey, distributed and recovered through the network, and the survey was conducted on all medical institutions (excluding military hospitals) conducting radiotherapy in Hunan province in 2022, and the quality and safety evaluation was checked by the Hunan Radiotherapy Quality Control Center using stratified sampling field inspection. The differences between the self-evaluation scores of radiotherapy quality and safety and the on-site inspection scores of each unit was compared using Wilcoxon test.Results:By the end of 2022, there were 76 medical institutions (excluding military hospitals) conducting radiotherapy in Hunan province, including 62 tertiary hospitals and 14 secondary hospitals, with a total of 44 253 radiotherapy patients admitted annually. The total number of personnel engaged in radiotherapy was 1 381, including 746 physicians, 205 physicists, 397 technicians and 33 maintenance engineers. There were a total of 88 accelerators (including 3 tomotherapy units), 10 gamma knives, and 28 rear-loading machines, with 1.33 gas pedals per million population. There were 36 units that were carrying out three-dimensional conformal technology, 60 static intensity modulation technology, 20 volumetric rotational intensity modulation, 27 stereotactic radiotherapy, 44 image-guided radiotherapy, 33 respiratory motion management, and 27 rear-loading radiotherapy. In the quality and safety evaluation situation, the basic requirements of radiotherapy specialty scored high, with 2 units achieving full marks and no failing units. Radiotherapy personnel and organization, radiotherapy process, documentation record score and other aspects of no full-score units, the score was concentrated in 60~＜80 points, and all have part of the unit failed.Conclusions:The radiotherapy industry in Hunan province has been developed steadily in recent years in general, and the structure of radiotherapy personnel tends to be reasonable, but there still exists uneven distribution of radiotherapy resources, poor utilization of equipment in some areas, and inadequate development of technology. The overall quality and safety evaluation are good, but there are still many deficiencies in the organizational requirements of radiotherapy personnel, process requirements and documentation, which need to be continuously optimized and improved in the future, and at the same time, field inspections will be intensified to ensure the quality and safety of radiotherapy.

Objective To predict risk factors affecting the prognosis of HIV/AIDS patients with Candida infection, providing clinicians with predictors for early identifying high-risk patients. Methods Clinical data were collected from HIV/AIDS patients with Candida infection admitted to an infectious disease hospital in Guangxi from January 2012 to June 2019. Patients were divided into a death group and a survival group according to their different prognostic outcomes. Cases were randomly selected and matched using propensity score matching (PSM) at a ratio of 1∶3 (death: survival) to construct the model. The data were split into a training set and a testing set at a ratio of 7∶3. Various machine learning models were built, and the optimal model was selected as the final prediction model by comprehensively evaluating the model performance. Finally, the SHAP values were used to interpret the features of the model and analyze the influencing factors of patients' prognostic outcomes. Results A total of 3 098 HIV/AIDS patients with Candida infection were collected. From 2012 to June 2019, the in-hospital mortality rate of HIV/AIDS patients with Candida infection showed a linear and stable downward trend (P=0.043). After applying PSM, data from 1 620 cases were used to construct six different machine learning models, among which the XGBoost model had the best performance (training/testing set, AUC=0.98/0.85, sensitivity=0.93/0.75, specificity=0.93/0.84). Respiratory failure, urea, and LDH levels were thought to be the three major factors affecting the prognostic outcomes of HIV/AIDS patients with Candida infection. Conclusions The XGBoost model showed good predictive performance in predicting prognostic outcomes of HIV/AIDS patients with Candida infection. The model can provide early warning for the identification of high-risk patients and assist clinicians to take personalized treatment measures promptly, which is of great significance for guiding clinical decision-making.

In order to establish a specific,rapid and convenient method for the detection of Campy-lobacter jejuni(C.jejuni).A set of specific primers and a probe that do not cause false positives were designed with the hipO gene of C.jejuni as the target,and the 5'ends of the downstream primers and probes were labeled with biotin and fluorescein,respectively.C.jejuni-RPA-LFD had no cross-reactivity with Klebsiella pneumoniae,Escherichia coli,Pseudomonas,Bacillus cereus,Pasteurella,Proteus mirabilis,and Salmonella typhimurium,and the optimal reaction system was 37 ℃,25 min,and its sensitivity could reach 3.93×100 copies/μL,and 1 × 102 CFU/mL of C.jejuni contaminated stool samples could be detected in the simulated detection.The C.jejuni-RPA-LFD established in this study has the advantages of good specificity,simplicity,rapidity and high sensitivity,which provides an effective way for the rapid diagnosis of C.jejuni and the con-trol of the spread of C.jejuni at the grassroots level of livestock and poultry farming.

Objective To analyze the spatial and temporal characteristics of hand, foot and mouth disease (HFMD) in Hunan Province from 2016 to 2020. Methods The data of HFMD in Hunan Province from 2016 to 2020 were collected from China's Disease Prevention and Control Information System. HFMD spatial autocorrelation analysis was conducted by ArcGIS 10.2 software at county level, and spatial-temporal scan statistical analysis was performed by SaTScan 9.7 software. Results A total of 714 157 cases was reported in Hunan Province during 2016-2020, with an average annual incidence rate of 208.36/100 000. Global spatial autocorrelation showed that HFMD had a positive spatial correlation on the county scale in Hunan Province during this period. Local spatial autocorrelation indicated that the hot spots were mainly concentrated in the north of central Hunan, the east of central Hunan and the west of Hunan. Spatial-temporal scanning analysis revealed the first class clusters (RR = 6.65, P< 0.001) covering 34 counties in northern and central Hunan, mainly distributed in Yueyang City, Changsha City, Zhuzhou City, Yiyang City and Xiangtan City from May 2018 to June, and the second class clusters (RR = 3.02, P < 0.001) covering 40 counties in western Hunan and central and southwest Hunan from April 2016 to June 2016. Conclusion HFMD incidence exhibits seasonal and regional characteristics in Hunan Province. The prevention and control of HFMD should be guided by combining the characteristics of spatial-temporal clustering.