ObjectiveTo analyze the infection status of main respiratory pathogens in influenza-like illness (ILI) cases in Anji County, Huzhou City, Zhejiang Province, and to provide a reference for the prevention, diagnosis, and treatment of respiratory infections. MethodsThroat swab samples were collected from 520 ILI cases in an influenza sentinel surveillance hospital in Anji County of Zhejiang Province from December 2023 to November 2024. Multiplex real-time fluorescence quantitative polymerase chain reaction (mRT-PCR) was used to detect 18 pathogens and their subtypes, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), influenza A virus (Flu A), influenza A (H1N1) virus, influenza A (H3N2) virus, influenza B virus (Flu B), influenza B virus Victoria lineage (BV), influenza B virus Yamagata lineage (BY), coronavirus (CoV), human parainfluenza virus (HPIV), respiratory syncytial virus (RSV), human metapneumovirus (HMPV), adenovirus (ADV), human bocavirus (HBoV), enterovirus (EV), rhinovirus (RV), Mycoplasma pneumoniae (MP), Chlamydia pneumoniae (CP), and Streptococcus pneumoniae (SP). ResultsThe overall positivity rate of pathogens in 520 samples was 33.65%, among which the detection rates of Flu (9.14%), ADV (7.50%), SARS-CoV-2 (6.15%), and EV (3.65%) were relatively high. There were statistically significant differences in the overall positivity rate of pathogens by age and season (all P<0.05). The highest overall positivity rate was observed in the 5‒14 years old group (42.77%), and the overall positivity rate in winter (53.08%) was significantly higher than that in other seasons. ConclusionFrom 2023 to 2024, the main respiratory pathogens detected in ILI cases in Anji County were Flu, ADV, SARS-CoV-2, and EV. The epidemic characteristics showed age and seasonal specificity, so it is necessary to strengthen prevention and control for high-risk populations and epidemic seasons in a targeted manner.

The diagnosis of Meige's syndrome predominantly relies on the clinical assessment by physicians.Given the complexity and similarity of its symptoms to other neurological disorders,the diagnosis is crucial for both doctors and patients.Herein a detection dataset for Meige's syndrome is compiled from video recordings of 31 patients,and an automated diagnostic system for Meige's syndrome(MS-Net)applicable to untrimmed videos is developed.The system utilizes RetinaNet and UNet3+to construct temporal detection and segmentation branches for multi-scale feature extraction and temporal segmentation,obtains probability vectors for detection windows and the probability of disease onset per frame via the decoding of temporal detection and segmentation branches,and finally generates a refined probability for each window by processing the probability predictions from both branches using a multi-layer perceptron.The model performance is optimized using additional loss functions and data augmentation techniques,operating on features interpretable by clinical physicians.MS-Net can assist in the diagnosis of Meige's syndrome,improving the accuracy,convenience,and efficiency of the early diagnosis.The comparison of MS-Net with other state-of-the-art networks indicates that MS-Net achieves comparable performance in terms of average precision while utilizing interpretable features required in clinical practice.

Gene synthesis is an enabling technology that supports the development of synthetic biology. The existing approaches for de novo gene synthesis generally have tedious operation, low efficiency, high error rates, and limited product lengths, being difficult to support the huge demand of synthetic biology. The assembly and error correction are the keys in gene synthesis. This study first designed the oligonucleotide sequences by reasonably splitting the virus genome of approximately 10 kb by balancing the parameters of sequence design software ability, PCR amplification ability, and assembly enzyme assembly ability. Then, two-step PCR was performed with high-fidelity polymerase to complete the de novo synthesis of 3.0 kb DNA fragments, and error correction reactions were performed with T7 endonuclease Ⅰ for the products from different stages of PCR. Finally, the virus genome was assembled by 3.0 kb DNA fragments from de novo synthesis and error correction and then sequenced. The experimental results showed that the proposed method successfully produced the DNA fragment of about 10 kb and reduced the probability of large fragment mutations during the assembly process, with the lowest error rate reaching 0.36 errors/kb. In summary, this study developed an efficient de novo method for synthesizing a viral genome of about 10 kb with T7 endonuclease Ⅰ-mediated error correction. This method enabled the synthesis of a 10 kb viral genome in one day and the correct plasmid of the viral genome in five days. This study optimized the de novo gene synthesis process, reduced the error rate, simplified the synthesis and assembly steps, and reduced the cost of viral genome assembly.
Genome, Viral/genetics* ; Polymerase Chain Reaction/methods* ; DNA, Viral/genetics* ; Bacteriophage T7/enzymology* ; Synthetic Biology/methods*

Objective To clarify the active ingredients and the potential molecular mechanism of Guben Qushi Huayu Prescription in treating psoriasis recurrence.Methods An ultra performance liquid chromatography-quadrupole time-of-flight mass spectrometry(UPLC-Q-TOF/MS)was applied to analyze the whole formula and the constituents absorbed into blood of Guben Qushi Huayu Prescription,and molecular docking technology was used to study the binding affinity of the constituents absorbed into blood with psoriasis-related immunomodulatory proteins such as CD69 and CD103 proteins.Results Mass spectrometry analysis identified 21 active ingredients such as paeoniflorin in Guben Qushi Huayu Prescription,including several known anti-inflammatory and immunomodulatory compounds.Analysis of the constituents absorbed into blood identified 11 ingredients,including paeoniflorin,that may affect the course of psoriasis through blood circulation.Molecular docking studies revealed that the constituents absorbed into blood,including astilbin,isoastilbin,chlorogenic acid,neochlorogenic acid,cryptochlorogenic acid,helicine,paeoniflorin,ononin,all had high binding affinities with CD69 and CD103 proteins.Conclusion This research reveals the main active ingredients of Guben Qushi Huayu Prescription and their potential mechanism for regulating the recurrence of psoriasis by mass spectrometry and molecular docking technology,contributing to providing scientific basis for further pharmacological research and clinical application.

Objective:To investigate the current status of application of stereotactic body radiation therapy (SBRT) in China, aiming to provide reference for promoting the development of this technology.Methods:From January to March 2024, a questionnaire was designed and distributed online, targeting member units of the Professional Committee of Stereotactic Radiosurgery Treatment, which covers 175 radiotherapy units in 30 provinces and regions nationwide. The survey focused on the current application of SBRT technology and its utilization in the treatment of early-stage non-small cell lung cancer (NSCLC). A statistical description of the survey results was presented.Results:Of 175 questionnaires distributed, a total of 130 valid responses were collected, with an effective response rate of 74.3%. A total of 81.5% (106/130) of the units had implemented SBRT technology, and 99.1% of the respondents believed it was necessary to further promote SBRT technology, yet the actual training rate was only 67.0%. SBRT equipment configuration: there were a total of 267 SBRT equipment, featuring a diverse range of types, with traditional linear accelerators as the mainstays, accounting for 76.0% ( n=203), followed by 12.0% ( n=32) for TOMO, 6.4% ( n=17) for Cyber knife, 3.7% ( n=10) for Gamma knife, and proton/heavy ion equipment at 1.5% ( n=4), respectively. The percentage of units with multi-leaf collimator leaf widths ≤0.5 cm was 93.4% (99/106). The application of SBRT: the first radiotherapy unit commenced SBRT in 2000, and this technology entered a period of rapid growth after 2015, sustaining a steady increase over the past decade; SBRT technology was mainly applied in the brain, lung, liver, bone, adrenal gland, and kidney, with application rates of 97.2%, 94.3%, 86.8%, 71.7%, 56.6%, and 27.4%, respectively, while the application rates for the pancreas, metastatic lymph nodes, and other parts were less than 5%. Current status of SBRT technology application in early-stage NSCLC: 90.6% (96/106) of units had implemented SBRT; pre-treatment multi-disciplinary diagnosis and treatment accounted for 77% (74/96); the proportion of application units for peripheral and central type lung cancer lesions both exceeded 57.3%, whereas the application rate for ultra-central type and lesions > 5 cm lung cancer was less than 30%; there was significant variability in the selection of reference guidelines, dose fractionation patterns, and the concept of central type among units. Conclusions:The development of SBRT technology in China is in a period of steady growth, but several issues such as low training rate and lack of standardization still exist. The survey results provide important reference for clinical training and promotion of SBRT technology in China.

To fully exploit Transformer for accurate registration,self-similarity context is used as a feature extractor to extract the semantic information of the voxel neighborhood context,using symmetric multi-scale discrete optimization with diffusion regularization to find smooth transformations for quickly calculating the point-by-point distance between descriptors.In addition,a spatial-channel Transformer based on window attention network is proposed,which combines channel,spatial attention and self-attention scheme based on(moving)window,and makes full use of the complementary advantages of these 3 attention mechanisms,enabling the network to utilize global statistical information and have strong local fitting ability.The results of comprehensive experiments on 3D brain MRI datasets of LPBA40,IXI and OASIS shows that the proposed method is superior to the commonly used registration methods(SyN,VoxelMorph,CycleMorph,ViT-V-Net and TransMorph)on several evaluation indicators,proving its effectiveness in deformable medical image registration.

The diagnosis of Meige's syndrome predominantly relies on the clinical assessment by physicians.Given the complexity and similarity of its symptoms to other neurological disorders,the diagnosis is crucial for both doctors and patients.Herein a detection dataset for Meige's syndrome is compiled from video recordings of 31 patients,and an automated diagnostic system for Meige's syndrome(MS-Net)applicable to untrimmed videos is developed.The system utilizes RetinaNet and UNet3+to construct temporal detection and segmentation branches for multi-scale feature extraction and temporal segmentation,obtains probability vectors for detection windows and the probability of disease onset per frame via the decoding of temporal detection and segmentation branches,and finally generates a refined probability for each window by processing the probability predictions from both branches using a multi-layer perceptron.The model performance is optimized using additional loss functions and data augmentation techniques,operating on features interpretable by clinical physicians.MS-Net can assist in the diagnosis of Meige's syndrome,improving the accuracy,convenience,and efficiency of the early diagnosis.The comparison of MS-Net with other state-of-the-art networks indicates that MS-Net achieves comparable performance in terms of average precision while utilizing interpretable features required in clinical practice.

To fully exploit Transformer for accurate registration,self-similarity context is used as a feature extractor to extract the semantic information of the voxel neighborhood context,using symmetric multi-scale discrete optimization with diffusion regularization to find smooth transformations for quickly calculating the point-by-point distance between descriptors.In addition,a spatial-channel Transformer based on window attention network is proposed,which combines channel,spatial attention and self-attention scheme based on(moving)window,and makes full use of the complementary advantages of these 3 attention mechanisms,enabling the network to utilize global statistical information and have strong local fitting ability.The results of comprehensive experiments on 3D brain MRI datasets of LPBA40,IXI and OASIS shows that the proposed method is superior to the commonly used registration methods(SyN,VoxelMorph,CycleMorph,ViT-V-Net and TransMorph)on several evaluation indicators,proving its effectiveness in deformable medical image registration.

Objective:To investigate the current status of application of stereotactic body radiation therapy (SBRT) in China, aiming to provide reference for promoting the development of this technology.Methods:From January to March 2024, a questionnaire was designed and distributed online, targeting member units of the Professional Committee of Stereotactic Radiosurgery Treatment, which covers 175 radiotherapy units in 30 provinces and regions nationwide. The survey focused on the current application of SBRT technology and its utilization in the treatment of early-stage non-small cell lung cancer (NSCLC). A statistical description of the survey results was presented.Results:Of 175 questionnaires distributed, a total of 130 valid responses were collected, with an effective response rate of 74.3%. A total of 81.5% (106/130) of the units had implemented SBRT technology, and 99.1% of the respondents believed it was necessary to further promote SBRT technology, yet the actual training rate was only 67.0%. SBRT equipment configuration: there were a total of 267 SBRT equipment, featuring a diverse range of types, with traditional linear accelerators as the mainstays, accounting for 76.0% ( n=203), followed by 12.0% ( n=32) for TOMO, 6.4% ( n=17) for Cyber knife, 3.7% ( n=10) for Gamma knife, and proton/heavy ion equipment at 1.5% ( n=4), respectively. The percentage of units with multi-leaf collimator leaf widths ≤0.5 cm was 93.4% (99/106). The application of SBRT: the first radiotherapy unit commenced SBRT in 2000, and this technology entered a period of rapid growth after 2015, sustaining a steady increase over the past decade; SBRT technology was mainly applied in the brain, lung, liver, bone, adrenal gland, and kidney, with application rates of 97.2%, 94.3%, 86.8%, 71.7%, 56.6%, and 27.4%, respectively, while the application rates for the pancreas, metastatic lymph nodes, and other parts were less than 5%. Current status of SBRT technology application in early-stage NSCLC: 90.6% (96/106) of units had implemented SBRT; pre-treatment multi-disciplinary diagnosis and treatment accounted for 77% (74/96); the proportion of application units for peripheral and central type lung cancer lesions both exceeded 57.3%, whereas the application rate for ultra-central type and lesions > 5 cm lung cancer was less than 30%; there was significant variability in the selection of reference guidelines, dose fractionation patterns, and the concept of central type among units. Conclusions:The development of SBRT technology in China is in a period of steady growth, but several issues such as low training rate and lack of standardization still exist. The survey results provide important reference for clinical training and promotion of SBRT technology in China.

Objective:To construct preeclampsia (PE) prediction models using information from the hospital electronic medical information and clinical laboratory data through logistic regression (LR) and machine learning algorithms, and to compare their predictive performance.Methods:The study was conducted based on the information from Rouji Pregnancy Test Database and the perinatal data of women who visited the Third Affiliated Hospital of Guangzhou Medical University from January 1, 2012, to December 31, 2019. Drawing upon clinical treatment guidelines and related literature, 28 clinical indicators from 2 736 pregnant women at 24 to 28 weeks of gestation were selected after a thorough integration and used for the construction of the PE prediction model dataset. Patients diagnosed with PE comprised the PE group ( n=245), while another 255 cases from the rest who did not have PE were selected, with undersampling method, as the control group. The Random Forest algorithm (RF), eXtreme Gradient Boosting (XGB) algorithm, and LR model were each employed to develop predictive models for PE. Following the construction of the models, external validation of PE prediction accuracy was carried out using data acquired from an independent prospective cohort study on PE that was conducted from June 2019 to December 2022, in which 38 PE cases and 80 controls were chosen. The performance of predictive models were evaluated using metrics such as accuracy, sensitivity, specificity, and the area under the curve (AUC) of receiver operating characteristic. Results:Indicators included in the construction of the three predictive models suggested that uric acid, creatinine, maternal age, early pregnancy body mass index, urea, triglycerides, red blood cell count, eosinophil count, total cholesterol, neutrophil count, urine protein, alanine aminotransferase, and urine occult blood were influential in PE prediction models. The AUCs for RF, XGB, and LR models in the training and test sets were 0.851 (95% CI:0.730-0.891), 0.955 (95% CI:0.865-0.987), 0.884 (95% CI:0.767-0.923) vs. 0.845 (95% CI:0.723-0.868), 0.907 (95% CI:0.791-0.919), 0.851 (95% CI:0.755-0.893), respectively. In the test set, the accuracy, sensitivity, and specificity for RF, XGB, and LR models were 0.803, 0.607, 0.958, 0.864, 0.790, 0.927, and 0.832, 0.661, 0.971, respectively. In the external validation of the RF, XGB and LR predictive models, the accuracy were 0.822, 0.814, and 0.763; the sensitivity were 0.737, 0.789, and 0.605, and the specificity were 0.863, 0.825, and 0.838, respectively. Among them, XGB model showed the highest Youden's index (0.614). Conclusion:Compared to traditional methods of model construction, machine learning algorithms can establish more effective PE prediction models using real clinical data.