Exploration of the correlation between facet joints cross-sectional area asymmetry and cervical disc herniation.

PURPOSE: To evaluate the association between facet joints cross-sectional area asymmetry (FCAA) and cervical intervertebral disc herniation (CDH). METHODS: Overall, we retrospectively recruited 390 consecutive patients with CDH who underwent surgical treatment at our institution and 50 normal participants. Clinical variables and radiological findings related to CDH were collected. RESULTS: Patients with CDH were more likely to have a higher absolute value of the facet asymmetry factor (FAF) (p < .001), in which the FAF value of the left group was significantly higher than the other groups (p < .001) and the right group was lower than the central group (p < .001). 9.62% (C3/4), 12.19% (C4/5), 8.70% (C5/6), and 8.14% (C6/7) were determined as cutoff values for each variable that maximized sensitivity and specificity. Furthermore, multivariate analysis showed that cross-sectional area asymmetry of the facet joint (FCAA) was an independent risk factor for the occurrence of CDH. Also, the Chi-square test showed a significant difference in the distribution of the degeneration classification of the disc between the facet-degenerated group and the nondegenerated group at C5/6 (p = 0.026) and C6/7 (p = 0.005) in the facet asymmetry (FA) group. CONCLUSIONS: FCAA is evaluated as an independent risk factor for CDH and associated with the orientation of disc herniation. And facet joint orientation may also play a role in cervical spine degeneration rather than facet joint tropism.

The development and validation of automated machine learning models for predicting lymph node metastasis in Siewert type II T1 adenocarcinoma of the esophagogastric junction.

Background: Lymph node metastasis (LNM) is considered an essential prognosis factor for adenocarcinoma of the esophagogastric junction (AEG), which also affects the treatment strategies of AEG. We aimed to evaluate automated machine learning (AutoML) algorithms for predicting LNM in Siewert type II T1 AEG. Methods: A total of 878 patients with Siewert type II T1 AEG were selected from the Surveillance, Epidemiology, and End Results (SEER) database to develop the LNM predictive models. The patients from two hospitals in Suzhou were collected as the test set. We applied five machine learning algorithms to develop the LNM prediction models. The performance of predictive models was assessed using various metrics including accuracy, sensitivity, specificity, the area under the curve (AUC), and receiver operating characteristic (ROC) curve. Results: Patients with LNM exhibited a higher proportion of male individuals, a poor degree of differentiation, and submucosal infiltration, with statistical differences. The deep learning (DL) model demonstrated relatively good accuracy (0.713) and sensitivity (0.868) among the five models. Moreover, the DL model achieved the highest AUC (0.781) and sensitivity (1.000) in the test set. Conclusion: The DL model showed good predictive performance among five AutoML models, indicating the advantage of AutoML in modeling LNM prediction in patients with Siewert type II T1 AEG.

Deep learning to predict esophageal variceal bleeding based on endoscopic images.

OBJECTIVE: Esophageal varix (EV) bleeding is a particularly serious complications of cirrhosis. Prediction of EV bleeding requires extensive endoscopy experience; it remains unreliable and inefficient. This retrospective cohort study evaluated the feasibility of using deep learning (DL) to predict the 12-month risk of EV bleeding based on endoscopic images. METHODS: Six DL models were trained to perform binary classification of endoscopic images of EV bleeding. The models were subsequently validated using an external test dataset, then compared with classifications performed by two endoscopists. RESULTS: In the validation dataset, EfficientNet had the highest accuracy (0.910), followed by ConvMixer (0.898) and Xception (0.875). In the test dataset, EfficientNet maintained the highest accuracy (0.893), which was better than the endoscopists (0.800 and 0.763). Notably, one endoscopist displayed higher recall (0.905), compared with EfficientNet (0.870). When their predictions were assisted by artificial intelligence, the accuracies of the two endoscopists increased by 17.3% and 19.0%. Moreover, statistical agreement among the models was dependent on model architecture. CONCLUSIONS: This study demonstrated the feasibility of using DL to predict the 12-month risk of EV bleeding based on endoscopic images. The findings suggest that artificial intelligence-aided diagnosis will be a useful addition to cirrhosis management.

V-doped Ni2P nanoparticle grafted g-C3N4 nanosheets for enhanced photocatalytic hydrogen evolution performance under visible light.

Exploring low-cost and highly active photocatalysts with noble metal-free cocatalysts is of great significance for photocatalytic hydrogen evolution under simulated sunlight irradiation. In this work, a novel V-doped Ni2P nanoparticle loaded g-C3N4 nanosheet is reported as a highly efficient photocatalyst for H2 evolution under visible light irradiation. The results demonstrate that the optimized 7.8 wt% V-Ni2P/g-C3N4 photocatalyst exhibits a high hydrogen evolution rate of 271.5 µmol g-1 h-1, which is comparable to that of the 1 wt% Pt/g-C3N4 photocatalyst (279 µmol g-1 h-1), and shows favorable hydrogen evolution stability for five successive runs within 20 h. The remarkable photocatalytic hydrogen evolution performance of V-Ni2P/g-C3N4 is mainly due to the enhanced visible light absorption ability, the facilitated separation of photo-generated electron-hole pairs, the prolonged lifetime of photo-generated carriers and the fast transmission ability of electrons.

Endoscopic full­thickness resection with clip­ and snare­assisted traction for gastric submucosal tumours in the fundus: A single­centre case series.

Exposed endoscopic full-thickness resection (Eo-EFTR) has been recognized as a feasible therapy for gastrointestinal submucosal tumours (SMTs) originating deep in the muscularis propria layer; however, Eo-EFTR is difficult to perform in a retroflexed fashion in the gastric fundus. As a supportive technique, clip- and snare-assisted traction may help expose the surgical field and shorten the operation time in endoscopic resection of difficult regions. However, the application of clip- and snare-assisted traction in Eo-EFTR of SMTs in the gastric fundus is limited. Between April 2018 and December 2021, Eo-EFTR with clip- and snare-assisted traction was performed in 20 patients with SMTs in the gastric fundus at The First Affiliated Hospital of Soochow University. The relevant clinical data were collected retrospectively for all of the patients and analysed. All 20 patients underwent Eo-EFTR successfully without conversion to open surgery or severe adverse events. The en bloc resection rate and R0 resection rate were both 100%. Two patients had abdominal pain and fever after the operation, and five patients had fever, which recovered with medical therapy. No complications, such as delayed bleeding or delayed perforation, were observed. The postoperative pathology indicated that 19 cases were gastrointestinal stromal tumours and one case was leiomyoma. During the follow-up, no residual tumour, local recurrence or distant metastasis was detected by endoscopy or abdominal computed tomography. In conclusion, Eo-EFTR with clip- and snare-assisted traction appears to be a relatively safe and effective treatment for gastric SMTs in the fundus. However, prospective studies on a larger sample size are required to verify the effect of the clip- and snare-assisted traction in Eo-EFTR.

Development and validation of a nomogram to predict the risk of surgical site infection within 1 month after transforaminal lumbar interbody fusion.

OBJECTIVE: Surgical site infection (SSI), a common serious complication within 1 month after transforaminal lumbar interbody fusion (TLIF), usually leads to poor prognosis and even death. The objective of this study is to investigate the factors related to SSI within 1 month after TLIF. We have developed a dynamic nomogram to change treatment or prevent infection based on accurate predictions. MATERIALS AND METHODS: We retrospectively analyzed 383 patients who received TLIF at our institution from January 1, 2019, to June 30, 2022. The outcome variable in the current study was the occurrence of SSI within 1 month after surgery. Univariate logistic regression analysis was first performed to assess risk factors for SSI within 1 month after surgery, followed by inclusion of significant variables at P < 0.05 in multivariate logistic regression analysis. The independent risk variables were subsequently utilized to build a nomogram model. The consistency index (C-index), calibration curve and receiver operating characteristic curve were used to evaluate the performance of the model. And the decision curve analysis (DCA) was used to analyze the clinical value of the nomogram. RESULTS: The multivariate logistic regression models further screened for three independent influences on the occurrence of SSI after TLIF, including lumbar paraspinal (multifidus and erector spinae) muscles (LPM) fat infiltration, diabetes and surgery duration. Based on the three independent factors, a nomogram prediction model was built. The area under the curve for the nomogram including these predictors was 0.929 in both the training and validation samples. Both the training and validation samples had high levels of agreement on the calibration curves, and the nomograms C-index was 0.929 and 0.955, respectively. DCA showed that if the threshold probability was less than 0.74, it was beneficial to use this nomograph to predict the risk of SSI after TLIF. In addition, the nomogram was converted to a web-based calculator that provides a graphical representation of the probability of SSI occurring within 1 month after TLIF. CONCLUSION: A nomogram including LPM fat infiltration, surgery duration and diabetes is a promising model for predicting the risk of SSI within 1 month after TLIF. This nomogram assists clinicians in stratifying patients, hence boosting decision-making based on evidence and personalizing the best appropriate treatment.

Automated Multimodal Machine Learning for Esophageal Variceal Bleeding Prediction Based on Endoscopy and Structured Data.

Esophageal variceal (EV) bleeding is a severe medical emergency related to cirrhosis. Early identification of cirrhotic patients with at a high risk of EV bleeding is key to improving outcomes and optimizing medical resources. This study aimed to evaluate the feasibility of automated multimodal machine learning (MMML) for predicting EV bleeding by integrating endoscopic images and clinical structured data. This study mainly includes three steps: step 1, developing deep learning (DL) models using EV images by 12-month bleeding on TensorFlow (backbones include ResNet, Xception, EfficientNet, ViT and ConvMixer); step 2, training and internally validating MMML models integrating clinical structured data and DL model outputs to predict 12-month EV bleeding on an H2O-automated machine learning platform (algorithms include DL, XGBoost, GLM, GBM, RF, and stacking); and step 3, externally testing MMML models. Furthermore, existing clinical indices, e.g., the MELD score, Child‒Pugh score, APRI, and FIB-4, were also examined. Five DL models were transfer learning to the binary classification of EV endoscopic images at admission based on the occurrence or absence of bleeding events during the 12-month follow-up. An EfficientNet model achieved the highest accuracy of 0.868 in the validation set. Then, a series of MMML models, integrating clinical structured data and the output of the EfficientNet model, were automatedly trained to predict 12-month EV bleeding. A stacking model showed the highest accuracy (0.932), sensitivity (0.952), and F1-score (0.879) in the test dataset, which was also better than the existing indices. This study is the first to evaluate the feasibility of automated MMML in predicting 12-month EV bleeding based on endoscopic images and clinical variables.

Mo-Doped Cu2S Multilayer Nanosheets Grown In Situ on Copper Foam for Efficient Hydrogen Evolution Reaction.

Metal sulfide electrocatalyst is developed as a cost-effective and promising candidate for hydrogen evolution reaction (HER). In this work, we report a novel Mo-doped Cu2S self-supported electrocatalyst grown in situ on three-dimensional copper foam via a facile sulfurization treatment method. Interestingly, Mo-Cu2S nanosheet structure increases the electrochemically active area, and the large fleecy multilayer flower structure assembled by small nanosheet facilitates the flow of electrolyte in and out. More broadly, the introduction of Mo can adjust the electronic structure, significantly increase the volmer step rate, and accelerate the reaction kinetics. As compared to the pure Cu2S self-supported electrocatalyst, the Mo-Cu2S/CF show much better alkaline HER performance with lower overpotential (18 mV at 10 mA cm-2, 322 mV at 100 mA cm-2) and long-term durability. Our work constructs a novel copper based in-situ metal sulfide electrocatalysts and provides a new idea to adjust the morphology and electronic structure by doping for promoting HER performance.

Formation of porous NiCoV-LTH nanosheet arrays by in situ etching of nickel foam for the hydrogen evolution reaction at large current density.

We report the in situ generation of NiCoV-LTH on nickel foam for the HER. Interestingly, the introduction of Co into NiV-LDH can induce the formation of porous nanosheets to expose a large number of active sites and change the electron density around Ni and V to promote the absorption of hydrogen species and thus accelerate the HER kinetics.