OBJECTIVE: To explore clinical efficacy of membrane-induced technique combined with gastrocnemius muscle flap transposition in treating traumatic osteomyelitis of the upper tibia. METHODS: A retrospective analysis was conducted on 7 patients with traumatic osteomyelitis of the upper tibia who were treated with membrane-induced technique combined with gastrocnemius muscle flap transposition from January 2022 to December 2023. Among them, there were 4 males and 3 females; aged from 29 to 57 years old; 4 patients were treated after open fracture, 2 patients were treated after closed fracture, and 1 patient was treated after scalding; the courses of disease ranges from 2 weeks to 8 years; sinus tracts were present in all patients, and the lesion range of the tibia ranged from 5 to 9 cm. The results of deep tissue bacterial culture showed that 2 patients were negative, 3 patients were staphylococcus aureus, 1 patient was methicillin-resistant staphylococcus aureus, and 1 patient was pseudomonas aeruginosa and 1 patient was klebsiella pneumoniae. After debridement, the range of bone defect ranged from 8 to 12 cm, and the cortical defect accounted for approximately 30% of the circumference. The area of soft tissue defect ranged from 8.0 cm×2.0 cm to 10.0 cm×6.0 cm. At the first stage, vancomycin-loaded/meropenem/gentamicin-loaded bone cement was implanted. The gastrocnemius muscle flap was repositioned to cover the wound surface and free skin grafting was performed. After an interval of 7 to 10 weeks, the stageⅡsurgery was performed to remove bone cement. Autologous iliac bone mixed with vancomycin/gentamicin and calcium sulfate artificial bone was transplanted, and the wound was sutured. One patient retained the original internal plants, one patient removed the internal plants and replaced them with steel plate external fixation, one patient replaced the internal plants and added steel plate external fixation, and three patients were simply fixed with steel plate external fixation. One year after operation, the recovery of knee joint and ankle joint functions was evaluated by using Hospital for Special Surgery (HSS) knee joint score and Kofoed ankle joint function score respectively. RESULTS: All patients had their wounds closed simultaneously with bone cement implantation and healed well. All patients were followed up for 12 to 17 months after operation, and satisfactory bone healing was achieved at 6 months after stageⅡsurgery. Twelve months after operation, all patients had good bone healing without obvious limping was observed when walking. At 12 months after operation HSS knee joint score ranged from 93 to 100 points, and Kofoed ankle function score ranged from 96 to 100 points. CONCLUSION For traumatic osteomyelitis of the upper tibia, a staged treatment plan combining membrane-induced technique and gastrocnemius flap transposition on the basis of thorough debridement could safely cover the wound surface, effectively control bone infection and achieve satisfactory bone healing, without adverse effects on limb function.
Humans ; Male ; Female ; Middle Aged ; Osteomyelitis/surgery* ; Adult ; Surgical Flaps ; Retrospective Studies ; Tibia/injuries* ; Muscle, Skeletal/surgery*

Inflammatory bowel disease (IBD) is a chronic inflammatory disorder of the gastrointestinal tract, which increases the incidence of colorectal cancer (CRC). In the pathophysiology of IBD, ubiquitination/deubiquitination plays a critical regulatory function. Josephin domain containing 2 (JOSD2), a deubiquitinating enzyme, controls cell proliferation and carcinogenesis. However, its role in IBD remains unknown. Colitis mice model developed by dextran sodium sulfate (DSS) or colon tissues from individuals with ulcerative colitis and Crohn's disease showed a significant upregulation of JOSD2 expression in the macrophages. JOSD2 deficiency exacerbated the phenotypes of DSS-induced colitis by enhancing colon inflammation. DSS-challenged mice with myeloid-specific JOSD2 deletion developed severe colitis after bone marrow transplantation. Mechanistically, JOSD2 binds to the C-terminal of inosine-5'-monophosphate dehydrogenase 2 (IMPDH2) and preferentially cleaves K63-linked polyubiquitin chains at the K134 site, suppressing IMPDH2 activity and preventing activation of nuclear factor kappa B (NF-κB) and inflammation in macrophages. It was also shown that JOSD2 knockout significantly exacerbated increased azoxymethane (AOM)/DSS-induced CRC, and AAV6-mediated JOSD2 overexpression in macrophages prevented the development of colitis in mice. These outcomes reveal a novel role for JOSD2 in colitis through deubiquitinating IMPDH2, suggesting that targeting JOSD2 is a potential strategy for treating IBD.

The tumor microenvironment is a crucial factor in tumor occurrence and progression. The hypoxic microenvironment is widely present in tumor tissue and is a key endogenous factor accelerating tumor deterioration. The "blood stasis toxin" theory, as an emerging perspective in tumor research, is regarded as the unique "soil" in tumor progression from the perspective of traditional Chinese medicine(TCM) due to its dynamic evolution mechanism, which closely resembles the formation of the hypoxic microenvironment. Scientifically integrating TCM theories with the biological characteristics of tumors and exploring precise syndrome differentiation and treatment strategies are key to achieving comprehensive tumor prevention and control. This article focused on the hypoxic microenvironment of the tumor, elucidating its formation mechanisms and evolutionary processes and carefully analyzing the internal relationship between the "blood stasis toxin" theory and the hypoxic microenvironment. Additionally, it explored the interaction among blood stasis, toxic pathogens, and hypoxic environment and proposed micro-level prevention and treatment strategies targeting the hypoxic microenvironment based on the "blood stasis toxin" theory, aiming to provide TCM-based theoretical support and therapeutic approaches for precise regulation of the hypoxic microenvironment.
Humans ; Tumor Microenvironment/drug effects* ; Neoplasms/therapy* ; Animals ; Medicine, Chinese Traditional ; Disease Progression ; Drugs, Chinese Herbal

In-depth study of the components of polymyxins is the key to controlling the quality of this class of antibiotics.Similarities and variations of components present significant analytical challenges.A two-dimensional(2D)liquid chromatography-mass spectrometry(LC-MS)method was established for screening and comprehensive profiling of compositions of the antibiotic colistimethate sodium(CMS).A high concentration of phosphate buffer mobile phase was used in the first-dimensional LC system to get the components well separated.For efficient and high-accuracy screening of CMS,a targeted method based on a self-constructed high resolution(HR)mass spectrum database of CMS components was established.The database was built based on the commercial MassHunter Personal Compound Database and Library(PCDL)software and its accuracy of the compound matching result was verified with six known components before being applied to genuine sample screening.On this basis,the unknown peaks in the CMS chromatograms were deduced and assigned.The molecular formula,group composition,and origins of a total of 99 compounds,of which the combined area percentage accounted for more than 95％of CMS components,were deduced by this 2D-LC-MS method combined with the MassHunter PCDL.This profiling method was highly efficient and could distinguish hundreds of components within 3 h,providing reliable results for quality control of this kind of complex drugs.

In-depth study of the components of polymyxins is the key to controlling the quality of this class of antibiotics. Similarities and variations of components present significant analytical challenges. A two-dimensional (2D) liquid chromatography-mass spectrometr (LC-MS) method was established for screening and comprehensive profiling of compositions of the antibiotic colistimethate sodium (CMS). A high concentration of phosphate buffer mobile phase was used in the first-dimensional LC system to get the components well separated. For efficient and high-accuracy screening of CMS, a targeted method based on a self-constructed high resolution (HR) mass spectrum database of CMS components was established. The database was built based on the commercial MassHunter Personal Compound Database and Library (PCDL) software and its accuracy of the compound matching result was verified with six known components before being applied to genuine sample screening. On this basis, the unknown peaks in the CMS chromatograms were deduced and assigned. The molecular formula, group composition, and origins of a total of 99 compounds, of which the combined area percentage accounted for more than 95% of CMS components, were deduced by this 2D-LC-MS method combined with the MassHunter PCDL. This profiling method was highly efficient and could distinguish hundreds of components within 3 h, providing reliable results for quality control of this kind of complex drugs.

An overview of the development of machine learning in medical imaging was presented.The current status of machine learning applied to CT and MRI imaging diagnosis and prognosis prediction of hepatocellular carcinoma(HCC)was summarized.The challenges faced by machine learning in the application of CT and MRI imaging diagnosis and prognosis prediction of HCC were analyzed and its future development directions were envisioned.[Chinese Medical Equipment Journal,2025,46(7):92-98]

Objective:Using methylation characteristics of human genes to construct machine learning predictive models for screening cervical cancer and precancerous lesions.Methods:Human DNA methylation detection was performed on 224 cervical exfoliated cell specimens from the Cancer Hospital of the Chinese Academy of Medical Sciences, Tianjin Central Hospital of Gynecology Obstetrics, Xinmi Maternal and Child Health Hospital of Henan Province, West China Second Affiliated Hospital of Sichuan University, and Heping Hospital Affiliated to Changzhi Medical College collected during April 2014 and March 2015. The hypermethylated gene fragments related to cervical cancer were selected by high-density, high-association, and hypermethylated gene fragment screening and the LASSO regression algorithm. Taking cervical intraepithelial neoplasia grade 2 (CIN2) or more severe lesions as the research outcome, machine learning predictive models based on the random forest (RF), naive Bayes (NB), and support vector machine (SVM) algorithm, respectively, were constructed. A total of 144 outpatient specimens were used as the training set and 80 cervical exfoliated cell specimens from women participating in the cervical cancer screening program were used as the test set to verify the predictive models. Using histological diagnosis results as the gold standard, the detection efficacy for CIN2 or more severe lesions of the three machine learning predictive models were compared with that of the human papilloma virus (HPV) detection and cytological diagnosis.Results:In the training set of 144 cases, there were 34 cases of HPV positivity, with a positive rate of 23.61%. Cytologically, there were 37 cases diagnosed as no intraepithelial lesion or malignancy (NILM), and 107 cases diagnosed as atypical squamous cells of undetermined significance (ASC-US) or above. Histologically, there were 28 cases without cervical intraepithelial neoplasia or benign cervical lesions, 31 cases of CIN1, 18 cases of CIN2, 31 cases of CIN3, and 36 cases of squamous cell carcinoma. Seven hypermethylated gene fragments were selected from 45 genes, and three machine learning prediction models based on the RF, NB, and SVM algorithm, respectively, were constructed. In the validation set of 80 cases, there were 28 cases of HPV positivity, with a positive rate of 35.00%. Cytologically, there were 65 cases diagnosed as NILM and 15 cases as ASC-US or above. Histologically, there were 39 cases without cervical intraepithelial neoplasia or benign cervical lesions, 10 cases of CIN1, 10 cases of CIN2, 11 cases of CIN3, and 10 cases of squamous cell carcinoma. In the validation set, the area under the curve (AUC) values of the RF model, NB model, SVM model, HPV detection, and cytological diagnosis of CIN2 or above were 0.90, 0.88, 0.82, 0.68, and 0.45, respectively. The DeLong test showed that there was no statistically significant difference in the AUC values between the RF, NB, and SVM models (all P＞0.05), and the AUC values of the RF and NB models were higher than that of HPV detection (both P＜0.01), and the AUC values of the RF, NB, and SVM models were higher than that of cytological diagnosis (all P＜0.01). Compared with the NB model, the sensitivity of the RF model was similar (80.65% vs. 77.42%), but the specificity of the NB model was much higher than that of the RF model (93.88% vs. 73.47%). Conclusion:Among the machine learning prediction models for cervical cancer and precancerous lesions constructed based on human DNA methylation, the NB model has good predictive performance for CIN2 and above lesions, and may be used for screening of cervical cancer and precancerous lesions.

An overview of the development of machine learning in medical imaging was presented.The current status of machine learning applied to CT and MRI imaging diagnosis and prognosis prediction of hepatocellular carcinoma(HCC)was summarized.The challenges faced by machine learning in the application of CT and MRI imaging diagnosis and prognosis prediction of HCC were analyzed and its future development directions were envisioned.[Chinese Medical Equipment Journal,2025,46(7):92-98]

Objective:Using methylation characteristics of human genes to construct machine learning predictive models for screening cervical cancer and precancerous lesions.Methods:Human DNA methylation detection was performed on 224 cervical exfoliated cell specimens from the Cancer Hospital of the Chinese Academy of Medical Sciences, Tianjin Central Hospital of Gynecology Obstetrics, Xinmi Maternal and Child Health Hospital of Henan Province, West China Second Affiliated Hospital of Sichuan University, and Heping Hospital Affiliated to Changzhi Medical College collected during April 2014 and March 2015. The hypermethylated gene fragments related to cervical cancer were selected by high-density, high-association, and hypermethylated gene fragment screening and the LASSO regression algorithm. Taking cervical intraepithelial neoplasia grade 2 (CIN2) or more severe lesions as the research outcome, machine learning predictive models based on the random forest (RF), naive Bayes (NB), and support vector machine (SVM) algorithm, respectively, were constructed. A total of 144 outpatient specimens were used as the training set and 80 cervical exfoliated cell specimens from women participating in the cervical cancer screening program were used as the test set to verify the predictive models. Using histological diagnosis results as the gold standard, the detection efficacy for CIN2 or more severe lesions of the three machine learning predictive models were compared with that of the human papilloma virus (HPV) detection and cytological diagnosis.Results:In the training set of 144 cases, there were 34 cases of HPV positivity, with a positive rate of 23.61%. Cytologically, there were 37 cases diagnosed as no intraepithelial lesion or malignancy (NILM), and 107 cases diagnosed as atypical squamous cells of undetermined significance (ASC-US) or above. Histologically, there were 28 cases without cervical intraepithelial neoplasia or benign cervical lesions, 31 cases of CIN1, 18 cases of CIN2, 31 cases of CIN3, and 36 cases of squamous cell carcinoma. Seven hypermethylated gene fragments were selected from 45 genes, and three machine learning prediction models based on the RF, NB, and SVM algorithm, respectively, were constructed. In the validation set of 80 cases, there were 28 cases of HPV positivity, with a positive rate of 35.00%. Cytologically, there were 65 cases diagnosed as NILM and 15 cases as ASC-US or above. Histologically, there were 39 cases without cervical intraepithelial neoplasia or benign cervical lesions, 10 cases of CIN1, 10 cases of CIN2, 11 cases of CIN3, and 10 cases of squamous cell carcinoma. In the validation set, the area under the curve (AUC) values of the RF model, NB model, SVM model, HPV detection, and cytological diagnosis of CIN2 or above were 0.90, 0.88, 0.82, 0.68, and 0.45, respectively. The DeLong test showed that there was no statistically significant difference in the AUC values between the RF, NB, and SVM models (all P＞0.05), and the AUC values of the RF and NB models were higher than that of HPV detection (both P＜0.01), and the AUC values of the RF, NB, and SVM models were higher than that of cytological diagnosis (all P＜0.01). Compared with the NB model, the sensitivity of the RF model was similar (80.65% vs. 77.42%), but the specificity of the NB model was much higher than that of the RF model (93.88% vs. 73.47%). Conclusion:Among the machine learning prediction models for cervical cancer and precancerous lesions constructed based on human DNA methylation, the NB model has good predictive performance for CIN2 and above lesions, and may be used for screening of cervical cancer and precancerous lesions.

Purpose: To investigate the effect of androgen deprivation therapy (ADT) on health-related quality of life (HRQOL) in Asian men with all stages of prostate cancer. Materials and Methods: READT (real-life evaluation of the effect of ADT in prostate cancer patients in Asia) was a multi-center, prospective observational study involving six sites across four Asian populations. We enrolled eligible prostate cancer patients, who opted for ADT alone or in combination without prior neoadjuvant or adjuvant ADT within 12 months. The EuroQoL-5 dimensions, 5 level scale (EQ-5D-5L) utility index scores and visual analog scale (VAS) were evaluated at baseline, month 6 and month 12. Results: A total of 504 patients were recruited into READT between September 2016 and May 2020 with 52.9% diagnosed with metastatic prostate cancer. The EQ-5D-5L was evaluable in 442/504 (87.7%) of patients. Overall baseline EQ-5D-5L utility index score was 0.924 (interquartile range [IQR] 0.876–1.000). We observed a statistically significant difference in baseline EQ-5D-5L utility index score among different populations with a median EQ-5D-5L utility index score of 1 for Taiwan & Hong Kong, 0.897 for China and 0.838 for Malaysia. Similar trend was observed throughout multiple treatment time-points. Stage IV prostate cancer were significantly associated with a lower baseline EQ-5D-5L utility index score compared to stage I–III prostate cancer, producing a median disutility value of -0.080. Participants had a high median VAS (80, IQR 70–90), indicating good overall health on average during ADT initiation. Conclusions The study highlights the differences in health state utility index scores among various Asian prostate cancer patients receiving ADT at real-world setting. Our findings will be informative and useful in cost-effectiveness evaluation and policy decision making.