Objective:To develop a predictive model for the risk of post-traumatic hydrocephalus (PTH) in patients with severe traumatic brain injury (sTBI) and validate its predictive performance.Methods:A retrospective case control study was conducted to analyze the clinical data of 580 sTBI patients admitted to the 904th Hospital of the Joint Logistics Support Force of the PLA between January 2016 and December 2023, including 413 males and 167 females, aged 18-88 years [(54.3±14.6)years]. Patients were stratified into PTH group ( n=195) and non-PTH group ( n=385), based on the presence of PTH within 6 months after injury. Data collected from the two groups such as general baseline indicators, TBI-related clinical indicators (including surgical data), laboratory findings, and radiological features. Except for the data collected during the operation, all the above data are the results of the first examination at admission. Univariate analysis and Lasso regression analysis were used to screen predictors for the risk of PTH in sTBI patients. Subsequent multivariate Logistic regression was employed to identify predictors and construct a regression equation. Based on this equation, a nomogram prediction model was developed using the R language. Model discrimination was estimated through the receiver operating characteristic (ROC) curve, and calibration performance via the Hosmer-Lemeshow (H-L) goodness-of-fit test and calibration curve. Moreover, decision curve analysis (DCA) and clinical impact curve (CIC) were used for evaluating the clinical utility of the model. Results:Univariate analysis revealed statistically significant differences in 37 variables between the two groups, including age, age group, heart rate, oxygen saturation, Glasgow coma scale (GCS) score, left pupil size, right pupil size, pupillary light reflex, intracranial pressure (ICP) monitoring, type of decompressive craniectomy, neutrophil count, lymphocyte count, monocyte count, red blood cell count, platelet count, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), neutrophil-lymphocyte-platelet ratio (N/LP), lymphocyte-to-monocyte ratio (LMR), systemic inflammation response index (SIRI), systemic immune-inflammation index (SII), fibrinogen (FIB), D-dimer, D-dimer-to-fibrinogen ratio (DFR), serum albumin, prognostic nutritional index, blood glucose, status of basal cisterns, midline shift, degree of midline shift, cerebral herniation, epidural hematoma (EDH), subdural hematoma (SDH), intraventricular hemorrhage (IVH), subarachnoid hemorrhage (SAH), modified Fisher grade, and skull fracture ( P<0.05). Lasso regression analysis identified 24 potential predictors for PTH, including age, GCS score, pupillary light reflex, type of decompressive craniectomy, monocyte count, platelet count, NLR, PLR, N/LP, LMR, SII, D-dimer, DFR, serum albumin, prognostic nutritional index, blood glucose, status of basal cisterns, degree of midline shift, cerebral herniation, EDH, SDH, IVH, modified Fisher grade and skull fracture. Multivariate Logistic regression analysis demonstrated that age, unilateral pupillary light reflex, absent pupillary light reflex, bilateral decompressive craniectomy, monocyte count, PLR, cerebral herniation, SDH, IVH, linear skull fracture and depressed skull fracture were independent risk factors for PTH. In contrast, serum albumin was identified as an independent protective factor for PTH ( P<0.05). The regression equation derived from these factors was: Logit[ P/(1- P)]=0.05×"age"+1.65×"unilateral pupillary light reflex"+2.79×"absent pupillary light reflex"+1.60×"bilateral decompressive craniectomy"+1.90×"monocyte count"+0.02×"PLR"-0.12×"serum albumin"+2.07×"cerebral herniation"+2.59×"SDH"+2.23×"IVH"+1.24×"linear skull fracture"+ 1.66×"depressed skull fracture"-22.61. The prediction model built upon this equation achieved an area under the ROC curve (AUC) of 0.95(95% CI 0.93, 0.97), with a sensitivity of 91.79%, specificity of 85.97%, and Youden′s index of 0.78. The H-L goodness-of-fit test indicated good calibration ( χ2=7.90, P=0.545). DCA results showed that the bias-corrected curve closely aligned with the actual curve and approximated the ideal curve, indicating a high clinical net benefit. Furthermore, CIC results demonstrated that with threshold probabilities greater than 60%, the number of patients identified as high-risk by the model highly corresponded with the actual number of patients who developed PTH. Conclusion:The prediction model incorporating age, unilateral pupillary light reflex, absent pupillary light reflex, bilateral decompressive craniectomy, monocyte count, PLR, serum albumin, cerebral herniation, SDH, IVH, linear skull fracture and depressed skull fracture exhibits robust predictive performance for PTH in sTBI patients.

Objective:To develop a predictive model for the risk of post-traumatic hydrocephalus (PTH) in patients with severe traumatic brain injury (sTBI) and validate its predictive performance.Methods:A retrospective case control study was conducted to analyze the clinical data of 580 sTBI patients admitted to the 904th Hospital of the Joint Logistics Support Force of the PLA between January 2016 and December 2023, including 413 males and 167 females, aged 18-88 years [(54.3±14.6)years]. Patients were stratified into PTH group ( n=195) and non-PTH group ( n=385), based on the presence of PTH within 6 months after injury. Data collected from the two groups such as general baseline indicators, TBI-related clinical indicators (including surgical data), laboratory findings, and radiological features. Except for the data collected during the operation, all the above data are the results of the first examination at admission. Univariate analysis and Lasso regression analysis were used to screen predictors for the risk of PTH in sTBI patients. Subsequent multivariate Logistic regression was employed to identify predictors and construct a regression equation. Based on this equation, a nomogram prediction model was developed using the R language. Model discrimination was estimated through the receiver operating characteristic (ROC) curve, and calibration performance via the Hosmer-Lemeshow (H-L) goodness-of-fit test and calibration curve. Moreover, decision curve analysis (DCA) and clinical impact curve (CIC) were used for evaluating the clinical utility of the model. Results:Univariate analysis revealed statistically significant differences in 37 variables between the two groups, including age, age group, heart rate, oxygen saturation, Glasgow coma scale (GCS) score, left pupil size, right pupil size, pupillary light reflex, intracranial pressure (ICP) monitoring, type of decompressive craniectomy, neutrophil count, lymphocyte count, monocyte count, red blood cell count, platelet count, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), neutrophil-lymphocyte-platelet ratio (N/LP), lymphocyte-to-monocyte ratio (LMR), systemic inflammation response index (SIRI), systemic immune-inflammation index (SII), fibrinogen (FIB), D-dimer, D-dimer-to-fibrinogen ratio (DFR), serum albumin, prognostic nutritional index, blood glucose, status of basal cisterns, midline shift, degree of midline shift, cerebral herniation, epidural hematoma (EDH), subdural hematoma (SDH), intraventricular hemorrhage (IVH), subarachnoid hemorrhage (SAH), modified Fisher grade, and skull fracture ( P<0.05). Lasso regression analysis identified 24 potential predictors for PTH, including age, GCS score, pupillary light reflex, type of decompressive craniectomy, monocyte count, platelet count, NLR, PLR, N/LP, LMR, SII, D-dimer, DFR, serum albumin, prognostic nutritional index, blood glucose, status of basal cisterns, degree of midline shift, cerebral herniation, EDH, SDH, IVH, modified Fisher grade and skull fracture. Multivariate Logistic regression analysis demonstrated that age, unilateral pupillary light reflex, absent pupillary light reflex, bilateral decompressive craniectomy, monocyte count, PLR, cerebral herniation, SDH, IVH, linear skull fracture and depressed skull fracture were independent risk factors for PTH. In contrast, serum albumin was identified as an independent protective factor for PTH ( P<0.05). The regression equation derived from these factors was: Logit[ P/(1- P)]=0.05×"age"+1.65×"unilateral pupillary light reflex"+2.79×"absent pupillary light reflex"+1.60×"bilateral decompressive craniectomy"+1.90×"monocyte count"+0.02×"PLR"-0.12×"serum albumin"+2.07×"cerebral herniation"+2.59×"SDH"+2.23×"IVH"+1.24×"linear skull fracture"+ 1.66×"depressed skull fracture"-22.61. The prediction model built upon this equation achieved an area under the ROC curve (AUC) of 0.95(95% CI 0.93, 0.97), with a sensitivity of 91.79%, specificity of 85.97%, and Youden′s index of 0.78. The H-L goodness-of-fit test indicated good calibration ( χ2=7.90, P=0.545). DCA results showed that the bias-corrected curve closely aligned with the actual curve and approximated the ideal curve, indicating a high clinical net benefit. Furthermore, CIC results demonstrated that with threshold probabilities greater than 60%, the number of patients identified as high-risk by the model highly corresponded with the actual number of patients who developed PTH. Conclusion:The prediction model incorporating age, unilateral pupillary light reflex, absent pupillary light reflex, bilateral decompressive craniectomy, monocyte count, PLR, serum albumin, cerebral herniation, SDH, IVH, linear skull fracture and depressed skull fracture exhibits robust predictive performance for PTH in sTBI patients.

Objective To evaluate the efficacy of soft channel minimally invasive puncture hematoma evacuation in treatment of hypertensive intracerebral hemorrhage based on intracranial pressure and blood-brain barrier index.Methods A total of 78 patients with hypertensive intracerebral hemorrhage who were admitted to No.904 Hospital of Joint Logistics Support Force of PLA from February 2019 to September 2021 were enrolled and divided into two groups according to different treatment methods.Control group(n=35)received craniotomy,and observation group(n=43)received soft channel minimally invasive puncture hematoma evacuation.Operation related indexes(operation time,intraoperative blood loss,and drainage time),intracranial pressure,blood-brain barrier index and therapeutic effect were compared between two groups.Results The observation group had shorter operation time,shorter drainage time and less intraoperative blood loss than the control group(P<0.05).Intracranial pressure at 24 h postoperatively was lower than that at the end of surgery in both groups(P<0.05).Intracranial pressure at the end of surgery and 24 h postoperatively in the observation group was lower than those in the control group(P<0.05).The blood-brain barrier index was decreased in both groups after surgery,and the index in the observation group was lower than that in the control group(P<0.05).The overall efficacy rate of the observation group was significantly higher than that of the control group(93.02%vs 74.29%,P<0.05).Conclusion Soft channel minimally invasive puncture hematoma evacuation can effectively reduce the intracranial pressure and blood-brain barrier index in patients with hypertensive intra cerebral hemorrhage,with shorter operation time and less intraoperative bleeding.It can be used for preferred surgical procedure for hypertensive intra cerebral hemorrhage.

Circular RNA (circRNA) is considered to be non-coding RNA due to the deletion of the 5' cap structure and lacks the function of encoding proteins or polypeptides. With the development of high-throughput transcriptome sequencing, ribosome sequencing and other technologies, researchers have discovered that there were short open reading frames (sORF) and internal ribosome entry sites (IRES) in the sequence of some circRNAs which can encode polypeptides or protein and play important roles in the proliferation of malignant tumors such as glioma, hepatoma, gastric cancer, breast cancer, and colon cancer. This paper reviews the coding function of circRNA and analyzes the role of its encoded production-polypeptides or protein in the proliferation mechanism of human malignant tumors.