Small cell lung cancer (SCLC) is a highly malignant disease that has garnered significant attention in terms of treatment modalities and course management. Gaining an understanding of the clinical characteristics of SCLC, acquiring proficiency in screening, diagnosis, and treatment methods for this condition, as well as promptly addressing any adverse reactions to treatment are essential foundations for developing a scientific and rational pathological management plan for SCLC. By utilizing an intelligent whole course follow-up management platform, dynamic follow-up, timely warnings, and early interventions can enable high-quality whole life cycle management. This article aims to review the current treatment landscape of SCLC while exploring the challenges associated with implementing a comprehensive process-oriented management approach. The goal is to provide valuable insights for better managing SCLC patients and ultimately improving their quality of life and prognosis.
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Humans ; Small Cell Lung Carcinoma/diagnosis* ; Lung Neoplasms/diagnosis* ; Quality of Life ; Follow-Up Studies

Objective:To develop a deep learning-based system for real-time recognition and classification of portal hypertensive gastropathy (PHG) and evaluate its ability to assist junior endoscopists.Methods:A total of 2 848 gastroscopy images from 832 patients with liver cirrhosis were selected from Digestive Endoscopy Center databases of Renmin Hospital of Wuhan University, Wuhan Hospital of Traditional Chinese and Western Medicine, and the Second Hospital of Jingzhou from January 2015 to October 2023. This system referred to 3 endoscopic features of Baveno Ⅱ scoring system. Three models were developed respectively for gastric antral vascular ectasia (GAVE), mosaic-like pattern (MLP), and red marks (RM). The specific classification references were as follows: (1) GAVE model: 0 no, 1 yes; (2) MLP model: 0 no, 1 mild, 2 severe; (3) RM model: 0 no, 1 isolated, 2 fused. The classification results for endoscopic characteristics of PHG of 3 endoscopy experts were taken as the gold standard. The yolov8-m model was used for training. The training dataset, validation dataset, and test dataset were allocated at a ratio of 8∶1∶1. The test dataset was used to evaluate the performance of models and their auxiliary effects on endoscopists. The accuracy, recall, precision, specificity and Kappa coefficient were calculated. Results:The accuracy, recall, specificity of GAVE model were 96.0% (48/50), 87.5% (7/8) and 97.6% (41/42). There was no significant difference between its accuracy and the gold standard ( χ2=316.226, P=1.000). The precision of GAVE1 and GAVE0 were 87.5% (7/8) and 97.6% (41/42) respectively. The accuracy of MLP model was 84.1% (132/157), and there was no significant difference compared with the gold standard ( χ2=3.286, P=0.193). The precision and recall of MLP2 were 88.2% (15/17) and 75.0% (15/20). The precision and recall of MLP1 were 77.9% (60/77) and 88.2% (60/68). The precision and recall of MLP0 were 90.5% (57/63) and 82.6% (57/69). The accuracy of RM model was 87.9% (123/140), and there was no significant difference compared with the gold standard ( χ2=2.891, P=0.409). The precision and recall of RM2 were 94.7% (18/19) and 78.3% (18/23). The precision and recall of RM1 were 72.2% (26/36) and 81.3% (26/32). The precision and recall of RM0 were 92.9% (79/85) and 92.9% (79/85). The mean accuracy of the three junior endoscopists, with and without the assistance of the GAVE model, MLP model, and RM model, respectively increased from 95.3% to 99.3%, from 83.9% to 91.9%, and from 81.9% to 83.1%. The overall consistency analysis of the 3 junior endoscopists with the gold standard indicated that the consistency of the GAVE model before and after assistance was extremely strong (both an overall Kappa of 1.000); the consistency before assistance of the MLP model was moderate (with an overall Kappa of 0.601), which increased to extremely strong after assistance (with an overall Kappa of 0.964); and the consistency of the RM model before and after assistance was also relatively strong (with an overall Kappa of 0.792 before and 0.798 after). Conclusion:The deep learning system accurately identifies and classifies PHG features and significantly enhances diagnostic performance of junior endoscopists.

Objective:To develop a deep learning-based system for real-time recognition and classification of portal hypertensive gastropathy (PHG) and evaluate its ability to assist junior endoscopists.Methods:A total of 2 848 gastroscopy images from 832 patients with liver cirrhosis were selected from Digestive Endoscopy Center databases of Renmin Hospital of Wuhan University, Wuhan Hospital of Traditional Chinese and Western Medicine, and the Second Hospital of Jingzhou from January 2015 to October 2023. This system referred to 3 endoscopic features of Baveno Ⅱ scoring system. Three models were developed respectively for gastric antral vascular ectasia (GAVE), mosaic-like pattern (MLP), and red marks (RM). The specific classification references were as follows: (1) GAVE model: 0 no, 1 yes; (2) MLP model: 0 no, 1 mild, 2 severe; (3) RM model: 0 no, 1 isolated, 2 fused. The classification results for endoscopic characteristics of PHG of 3 endoscopy experts were taken as the gold standard. The yolov8-m model was used for training. The training dataset, validation dataset, and test dataset were allocated at a ratio of 8∶1∶1. The test dataset was used to evaluate the performance of models and their auxiliary effects on endoscopists. The accuracy, recall, precision, specificity and Kappa coefficient were calculated. Results:The accuracy, recall, specificity of GAVE model were 96.0% (48/50), 87.5% (7/8) and 97.6% (41/42). There was no significant difference between its accuracy and the gold standard ( χ2=316.226, P=1.000). The precision of GAVE1 and GAVE0 were 87.5% (7/8) and 97.6% (41/42) respectively. The accuracy of MLP model was 84.1% (132/157), and there was no significant difference compared with the gold standard ( χ2=3.286, P=0.193). The precision and recall of MLP2 were 88.2% (15/17) and 75.0% (15/20). The precision and recall of MLP1 were 77.9% (60/77) and 88.2% (60/68). The precision and recall of MLP0 were 90.5% (57/63) and 82.6% (57/69). The accuracy of RM model was 87.9% (123/140), and there was no significant difference compared with the gold standard ( χ2=2.891, P=0.409). The precision and recall of RM2 were 94.7% (18/19) and 78.3% (18/23). The precision and recall of RM1 were 72.2% (26/36) and 81.3% (26/32). The precision and recall of RM0 were 92.9% (79/85) and 92.9% (79/85). The mean accuracy of the three junior endoscopists, with and without the assistance of the GAVE model, MLP model, and RM model, respectively increased from 95.3% to 99.3%, from 83.9% to 91.9%, and from 81.9% to 83.1%. The overall consistency analysis of the 3 junior endoscopists with the gold standard indicated that the consistency of the GAVE model before and after assistance was extremely strong (both an overall Kappa of 1.000); the consistency before assistance of the MLP model was moderate (with an overall Kappa of 0.601), which increased to extremely strong after assistance (with an overall Kappa of 0.964); and the consistency of the RM model before and after assistance was also relatively strong (with an overall Kappa of 0.792 before and 0.798 after). Conclusion:The deep learning system accurately identifies and classifies PHG features and significantly enhances diagnostic performance of junior endoscopists.

The development and change patterns as well as the disease course management of multiple ground-glass nodules(GGNs)in the lungs are currently hotspots and difficulties in clinical lung cancer research.Understanding the latest advancements in the natural history of multiple GGNs is crucial for grasping the disease variation patterns and formulat-ing management strategies.Meanwhile,utilizing advanced methods such as intelligent follow-up management platforms makes the long-term standardized management of GGNs possible.Therefore,this article provides an overview of the latest research advancements on the natural history of multiple GGNs and new experience in GGNs management.

With the performance improvement of the medical electronics and the progress of social development,the utilization rate of implantable medical electronic devices in China shows an increasing trend in recent years,and will maintain a growing trend in the future as population aging is accelerating.Even though implantable medical electronic devices have extremely low failure rates,the explicit clinical accidents caused by their reliability issues deserve sufficient attention in consideration of the large quantity of patients.Compared with other countries,there is lack of domestic researches on application risk of implantable medical electronic devices due to radiation therapy and diagnosis,which is reflected in not only the clinical research,but also the researches on the underlying physical damage mechanism and macro performance of the devices.Therefore,it is crucial and urgent to explore the application risk of implantable medical electronic devices caused by radiation therapy and diagnosis,which has high clinical and scientific significance.The study conducts a literature survey on the risks of medical electronic devices in the radiation environment generated by clinical treatment,summarizes the risks encountered in the aspects of total dose effect,electromagnetic compatibility and instantaneous effect,analyzes the above behaviors from the physical mechanism and perspective,and puts forward some meaningful suggestions for medical electronic engineering and clinical treatment.

With the performance improvement of the medical electronics and the progress of social development,the utilization rate of implantable medical electronic devices in China shows an increasing trend in recent years,and will maintain a growing trend in the future as population aging is accelerating.Even though implantable medical electronic devices have extremely low failure rates,the explicit clinical accidents caused by their reliability issues deserve sufficient attention in consideration of the large quantity of patients.Compared with other countries,there is lack of domestic researches on application risk of implantable medical electronic devices due to radiation therapy and diagnosis,which is reflected in not only the clinical research,but also the researches on the underlying physical damage mechanism and macro performance of the devices.Therefore,it is crucial and urgent to explore the application risk of implantable medical electronic devices caused by radiation therapy and diagnosis,which has high clinical and scientific significance.The study conducts a literature survey on the risks of medical electronic devices in the radiation environment generated by clinical treatment,summarizes the risks encountered in the aspects of total dose effect,electromagnetic compatibility and instantaneous effect,analyzes the above behaviors from the physical mechanism and perspective,and puts forward some meaningful suggestions for medical electronic engineering and clinical treatment.

Objective To investigate the effect of different optimization algorithms on accurate reconstruction of traffic accidents. Methods Non-dominated sorting genetic algorithm-II ( NSGA-II), neighborhood cultivation genetic algorithm (NCGA) and multi-objective particle swarm optimization (MOPSO) were used to optimize the multi-rigid body dynamic reconstruction of a real case. The effects of different optimization algorithms on convergence speed and optimal approximate solution were studied. The optimal initial impact parameters were simulated as boundary conditions of finite element method, and the simulated results were compared with the actual injuries. Results NCGA had a faster convergence speed and a better result in optimization process. The kinematic response of pedestrian vehicle collision reconstructed by the optimal approximate solution was consistent with the surveillance video. The prediction of craniocerebral injury was basically consistent with the cadaver examination. Conclusions The combination of optimization algorithm, rigid multibody and finite element method can complete the accurate reconstruction of traffic accidents and reduce the influence of human factors.

Metabolic reprogramming, a newly recognized trait of tumor biology, is an intensively studied prospect for oncology medicines. For numerous tumors and cancer cell subpopulations, oxidative phosphorylation (OXPHOS) is essential for their biosynthetic and bioenergetic functions. Cancer cells with mutations in isocitrate dehydrogenase 1 (IDH1) exhibit differentiation arrest, epigenetic and transcriptional reprogramming, and sensitivity to mitochondrial OXPHOS inhibitors. In this study, we report that berberine, which is widely used in China to treat intestinal infections, acted solely at the mitochondrial electron transport chain (ETC) complex I, and that its association with IDH1 mutant inhibitor (IDH1^mi) AG-120 decreased mitochondrial activity and enhanced antileukemic effect in vitro andin vivo. Our study gives a scientific rationale for the therapy of IDH1 mutant acute myeloid leukemia (AML) patients using combinatory mitochondrial targeted medicines, particularly those who are resistant to or relapsing from IDH1^mi.
Humans ; Oxidative Phosphorylation ; Berberine ; Electron Transport ; Mitochondria ; Leukemia, Myeloid, Acute ; Isocitrate Dehydrogenase

Objective:To study on the effects of gastrointestinal function and portal vein hemodynamics applicated with abdominal hot compressing with evodiae fructus and crude salt after hepatectomy.Methods:A total of 60 patients who underwent hepatectomy were randomly divided into 2 groups by random number table method, with 30 in each group. The control group was treated with conventional basic western medicine, while the treatment group was treated with medicinal evodiae fructus and crude salt hot compress on abdomen on the basis of the control group. The portal vein diameter (PVD), portal venous flow velocity (PVV), recovery time of main clinical indexes, clinical symptom scores and liver function indexes were dynamically monitored at different observation time points.Results:The scores of abdominal distension, nausea and vomiting in the treatment group were significantly lower than those in the control group 3 days after treatment ( t values were -3.489 and -2.740, respectively, all Ps<0.05). The recovery time of bowel sounds, first exhaust time and first defecation time in the treatment group were significantly earlier than those in the control group ( t values were -3.622, -4.297 and -4.151, respectively, all Ps<0.01). With the extension of treatment time, ALT in 2 groups showed a gradual downward trend ( P<0.05 or P<0.01), DBIL in control group was significantly higher at 3 days after treatment than before ( t=-2.157, P=0.039), and TBIL was significantly lower at 7 days after treatment than before ( t=2.175, P=0.038). The PVD ( t values were 3.528, 2.160) and PVV ( t values were 11.096, 4.264) of the treatment group were significantly higher than those of the control group 3 and 5 days after treatment ( P<0.01 or P<0.05). Conclusion:Early application of abdominal hot compressing with evodiae fructus and crude salt hot compress on abdomen after hepatectomy can improve the portal vein blood circulation and promote the rehabilitation of gastrointestinal function in patients with hepatectomy.