Objective To detect the isocenter of a medical linear accelerator using an optical surface monitoring system (OSMS) combined with a self-made 3D-printed phantom, and compare with the conventional front pointer measurement method. Methods A phantom that could be fixed to the front pointer of the accelerator was fabricated using 3D printing technology. The front pointer method and OSMS were used to detect the rotation isocenter deviations of the accelerator. During the experiments, the collimator, gantry, and couch linear displacement readings were recorded at different angles. We also recorded the measurement time of different surveyors. Results Compared to the front pointer measurement method, the OSMS method showed lower standard deviations. The reductions in standard deviations showed statistical significance in the Lat direction for the collimator rotation isocenter as well as in the Lat and Vrt directions for the gantry rotation isocenter. The mean deviation of Mag values for the collimator rotation isocenter using the OSMS method was 0.50 mm, which was larger than the 0.42 mm using the front pointer method. However, the standard deviation was 0.06 using the OSMS method, which was smaller than the 0.18 using the front pointer method. The mean deviation and standard deviation of Sag values for the gantry rotation isocenter using the OSMS method were 1.03 mm and 0.12, respectively, which were both larger than 0.47 mm and 0.06 using the front pointer method. For the couch rotation isocenter, the mean deviation and standard deviation using the OSMS method were 0.32 mm and 0.04, respectively, which were both smaller than the 0.55 mm and 0.20 using the front pointer method. All surveyors spent less time in isocenter measurements using the OSMS method compared to the front pointer method. Conclusion Compared with the front pointer method, the OSMS method significantly reduces the standard deviation among surveyors, effectively reduces the influence of the angle deviation of the accelerator on the measurement results, reduces detection time, improves detection efficiency, and provides a powerful practical basis for the application of OSMS in detecting the mechanical accuracy of the linear accelerator.

Objective To detect the isocenter of a medical linear accelerator using an optical surface monitoring system (OSMS) combined with a self-made 3D-printed phantom, and compare with the conventional front pointer measurement method. Methods A phantom that could be fixed to the front pointer of the accelerator was fabricated using 3D printing technology. The front pointer method and OSMS were used to detect the rotation isocenter deviations of the accelerator. During the experiments, the collimator, gantry, and couch linear displacement readings were recorded at different angles. We also recorded the measurement time of different surveyors. Results Compared to the front pointer measurement method, the OSMS method showed lower standard deviations. The reductions in standard deviations showed statistical significance in the Lat direction for the collimator rotation isocenter as well as in the Lat and Vrt directions for the gantry rotation isocenter. The mean deviation of Mag values for the collimator rotation isocenter using the OSMS method was 0.50 mm, which was larger than the 0.42 mm using the front pointer method. However, the standard deviation was 0.06 using the OSMS method, which was smaller than the 0.18 using the front pointer method. The mean deviation and standard deviation of Sag values for the gantry rotation isocenter using the OSMS method were 1.03 mm and 0.12, respectively, which were both larger than 0.47 mm and 0.06 using the front pointer method. For the couch rotation isocenter, the mean deviation and standard deviation using the OSMS method were 0.32 mm and 0.04, respectively, which were both smaller than the 0.55 mm and 0.20 using the front pointer method. All surveyors spent less time in isocenter measurements using the OSMS method compared to the front pointer method. Conclusion Compared with the front pointer method, the OSMS method significantly reduces the standard deviation among surveyors, effectively reduces the influence of the angle deviation of the accelerator on the measurement results, reduces detection time, improves detection efficiency, and provides a powerful practical basis for the application of OSMS in detecting the mechanical accuracy of the linear accelerator.

Objective To detect the isocenter of a medical linear accelerator using an optical surface monitoring system (OSMS) combined with a self-made 3D-printed phantom, and compare with the conventional front pointer measurement method. Methods A phantom that could be fixed to the front pointer of the accelerator was fabricated using 3D printing technology. The front pointer method and OSMS were used to detect the rotation isocenter deviations of the accelerator. During the experiments, the collimator, gantry, and couch linear displacement readings were recorded at different angles. We also recorded the measurement time of different surveyors. Results Compared to the front pointer measurement method, the OSMS method showed lower standard deviations. The reductions in standard deviations showed statistical significance in the Lat direction for the collimator rotation isocenter as well as in the Lat and Vrt directions for the gantry rotation isocenter. The mean deviation of Mag values for the collimator rotation isocenter using the OSMS method was 0.50 mm, which was larger than the 0.42 mm using the front pointer method. However, the standard deviation was 0.06 using the OSMS method, which was smaller than the 0.18 using the front pointer method. The mean deviation and standard deviation of Sag values for the gantry rotation isocenter using the OSMS method were 1.03 mm and 0.12, respectively, which were both larger than 0.47 mm and 0.06 using the front pointer method. For the couch rotation isocenter, the mean deviation and standard deviation using the OSMS method were 0.32 mm and 0.04, respectively, which were both smaller than the 0.55 mm and 0.20 using the front pointer method. All surveyors spent less time in isocenter measurements using the OSMS method compared to the front pointer method. Conclusion Compared with the front pointer method, the OSMS method significantly reduces the standard deviation among surveyors, effectively reduces the influence of the angle deviation of the accelerator on the measurement results, reduces detection time, improves detection efficiency, and provides a powerful practical basis for the application of OSMS in detecting the mechanical accuracy of the linear accelerator.

The conventional identification and prediction for failures of medical equipment mainly depend on experience and knowledge of manager for equipment,which are not able to be quantified and have lower efficiency. Therefore,it is obvious that the prediction for the failure of medical equipment is not accurate. With the technical development of computer and machine learning,the conventional identification and prediction that depend on experiences can deal with characteristics of failures through machine learning method to improve efficiency,which are hopeful in filling the gap of discipline about the identification and prediction for failures of medical equipment. This article summarized the application situation of machine learning in identifying and predicting failures of the medical equipment and the similarly electric equipment at home and abroad. Based on the key technique of identification and prediction,this article proposed suggestion about corresponding design architecture. According to the characteristics of the failure of medical equipment,this article summarized a series of information about algorithms of various machine learning in scene and accurate rate of identification and prediction,so as to provide references for relevant research of this field.

The conventional identification and prediction for failures of medical equipment mainly depend on experience and knowledge of manager for equipment,which are not able to be quantified and have lower efficiency. Therefore,it is obvious that the prediction for the failure of medical equipment is not accurate. With the technical development of computer and machine learning,the conventional identification and prediction that depend on experiences can deal with characteristics of failures through machine learning method to improve efficiency,which are hopeful in filling the gap of discipline about the identification and prediction for failures of medical equipment. This article summarized the application situation of machine learning in identifying and predicting failures of the medical equipment and the similarly electric equipment at home and abroad. Based on the key technique of identification and prediction,this article proposed suggestion about corresponding design architecture. According to the characteristics of the failure of medical equipment,this article summarized a series of information about algorithms of various machine learning in scene and accurate rate of identification and prediction,so as to provide references for relevant research of this field.

Objective To compare the positioning errors,required planning target volume(PTV)outer expansion boundary and three-dimensional displacement of two different positioning fixation devices CIVCO Uni-frame carbon fiber adjustable angle head frame and domestic integrated board in radiotherapy for the pa-tients with head tumor,and to investigate the application value of CIVCO headrest in the positioning of head tumor radiotherpay.Methods The clinical data of 82 patients with head tumors who underwent radiotherapy in this hospital from January to December 2023 were analyzed retrospectively.The patients were divided into the adjustable angle head frame group and integrated plate group,41 cases in each group.Both groups were fixed with U-shaped thermoplastic head film.Radiotherapy was performed by using ELEKTA infinity linear accelerator.The cone beam computed tomography(CBCT)image guidance was used,combined with bone reg-istration and manual calibration to obtain the patient positioning errors in three directions:X axis,Y axis and Z axis.The positioning error data of each CBCT was recorded;based on the PTV outward expansion boundary formula(2.5∑+0.7σ),the PTV outward expansion boundaries required by the two fixation devices in all di-rections for the treatment of head tumors were calculated;the 3-dimensional spatial displacement was calculat-ed based on the patient's single radiotherapy positioning error.Results The absolute positioning errors in the X,Y,and Z directions of the adjustable angle head frame group were 0.13(0.07,0.22),0.16(0.16,0.27),0.10(0.05,0.18)cm respectively,which in the integrated board group were 0.14(0.07,0.24),0.18(0.08,0.32),0.09(0.04,0.10)cm respectively.The absolute error of the Y direction in the adjustable angle head frame group was smaller than that of the integrated board group(P=0.006),and the difference in the relative error between the two groups was not statistically significant(P＞0.05).According to the PTV expansion formula,the calculated expansion boundaries of the X,Y,and Z directions in the adjustable head frame group were 0.24,0.30,0.22 cm,which in the integrated plate group were 0.21,0.36,0.21 cm,respectively.The three-dimensional space comprehensive positioning error value could be calculated by the formula calculation using the positioning error values in three directions.The mean values of the three-dimensional space positio-ning errors of the adjustable angle headstock group and domestic integrated plate group were 0.320 cm and 0.340 cm respectively,there was no statistically significant difference between the two groups.Conclusion The absolute error in Y direction and the median value of three-dimensional displacement of the CIVCO adjustable angle head rest are smaller than those of the domestic integrated plate,which is worthy of clinical promoted applica-tion if conditions permit.

Soil cadmium (Cd) pollution is one of the major environmental problems globally. Ophiopogon japonicus, a multifunctional plant extensively used in traditional Chinese medicine, has demonstrated potential in environmental remediation. This study investigated the Cd accumulation pattern of O. japonicus under cadmium stress and identified the heavy metal ATPase (HMA) family members in this plant. Our results demonstrated that O. japonicus exhibited a Cd enrichment factor (EF) of 2.75, demonstrating strong potential for soil Cd pollution remediation. Nine heavy metal ATPase (HMA) members of P1B-ATPases were successfully identified from the transcriptome data of O. japonicus, with OjHMA1-OjHMA6 classified as the Zn/Co/Cd/Pb-ATPases and OjHMA7-OjHMA9 as the Cu/Ag-ATPases. The expression levels of OjHMA1, OjHMA2, OjHMA3, and OjHMA7 were significantly up-regulated under Cd stress, highlighting their crucial roles in cadmium ion absorption and transport. The topological analysis revealed that these proteins possessed characteristic transmembrane (TM) segments of the family, along with functional A, P, and N domains involved in regulating ion absorption and release. Metal ion-binding sites (M4, M5, and M6) existed on the TM segments. Based on the number of transmembrane domains and the residues at metal ion-binding sites, the plant HMA family members were categorized into three subgroups: P1B-1 ATPases, P1B-2 ATPases, and P1B-4 ATPases. Specifically, the P1B-1 ATPase subgroup included the motifs TM4(CPC), TM5(YN[X]₄P), and TM6(M[XX]SS); the P1B-2 ATPase subgroup featured the motifs TM4(CPC), TM5(K), and TM6(DKTGT); the P1B-4 ATPase subgroup contained the motifs TM4(SPC) and TM6(HE[X]GT), all of which were critical for protein functions. Molecular docking results revealed the importance of conserved sequences such as CPC/SPC, DKTGT, and HE[X]GT in metal ion coordination and stabilization. These findings provide potential molecular targets for enhancing Cd uptake and tolerance of O. japonicus by genetic engineering and lay a theoretical foundation for developing new cultivars with high Cd accumulation capacity.
Cadmium/metabolism* ; Adenosine Triphosphatases/metabolism* ; Ophiopogon/drug effects* ; Soil Pollutants/toxicity* ; Plant Proteins/metabolism* ; Stress, Physiological ; Multigene Family ; Gene Expression Regulation, Plant

Chronic hepatitis B virus (HBV) infection remains a major global public health concern. The persistent presence of intrahepatic covalently closed circular DNA (cccDNA) poses a major challenge to the complete cure of chronic HBV infection. Therefore, identifying reliable and effective serological surrogate markers for cccDNA holds great clinical significance in evaluating antiviral efficacy, predicting prognosis, and guiding the clinical management of chronic HBV-infected patients. In recent years, serum HBV RNA has emerged as a promising non-invasive alternative marker for cccDNA, offering the potential for monitoring disease progression and predicting prognosis in chronic HBV-infected patients. In this review, we summarize recent studies on HBV RNA, highlighting its ability to assess the immune and histological status of patients, and discussing its value in guiding the timing of antiviral therapy. Furthermore, we systematically summarize the clinical significance of HBV RNA in multiple domains: monitoring viral replication, evaluating antiviral treatment efficacy, predicting relapse after treatment cessation, and guiding new antiviral strategies. This review aims to provide clinicians with valuable insights for better utilizing this marker in the clinical management of chronic HBV infection.

In recent years, cardiovascular disease has become a common disease. With the development of machine learning and big data technologies, the processing ability of electrocardiogram (ECG) signals has been greatly enhanced through new computer technologies, enabling the auxiliary diagnosis technology for cardiovascular disease (CVD) to achieve new improvements. This article discusses the application of machine learning in ECG processing, especially in the auxiliary diagnosis of diseases. Firstly, the conventional signal preprocessing methods are introduced, and then the EEG signal processing methods based on feature extraction and fuzzy classification are explored. Secondly, the application of auxiliary diagnosis in CVD is further summarized. Finally, the advantages and disadvantages of the two methods are analyzed, and based on this, a design of an auxiliary diagnostic system compatible with the two methods is proposed, providing a new perspective for similar applied researches in the future.
Machine Learning ; Cardiovascular Diseases/diagnosis* ; Humans ; Electrocardiography ; Signal Processing, Computer-Assisted ; Diagnosis, Computer-Assisted ; Fuzzy Logic ; Electroencephalography

Stroke is one of the common cerebrovascular diseases,which can be classified into acute(0-24 h),subacute(24 h-2 weeks)and chronic stage(>2 weeks).High-resolution T1-weighted imaging(T1WI)is one kind of magnetic resonance imaging(MRI)technique,which is main imaging mode of assessing the change of cerebral structure after stroke at chronic stage,and it can accurately judge the location and size of lesion.The precise segmentation for lesion is we accurately analyze dataset of neuroimaging in stroke with large scale,and is the key of predicting rehabilitation outcomes.The automatic segmentation algorithm is one kind of technique that can automatically segment data into many independent regions or object by specific regulation or model,which does not need manual intervention.Compared with manual segmentation,it has repeatable and scalable advantages,which does not need manual setting parameter.With the development of artificial intelligence technology,its application is gradually widespread in the field of medical imaging analysis.Adopting automatic segmentation algorithm to conduct reliable and repeatable segmentation for the lesions in T1WI,which can effectively assist clinical diagnosis and rehabilitation treatment for stroke.This review summarized the advance research of automatic segmentation algorithm based on T1WI for the lesion of chronic stroke from two aspects included conventional machine learning and deep learning,and the optimization approach of three kinds of algorithm structures,which can provide reference and inspiration for researching small lesion with higher difficulty in the field of automatic segmentation.