OBJECTIVE: To block the transmission of Cranio-facial-nasal syndrome (CFNS) caused by a large deletion of the EFNB1 gene through preimplantation genetic testing for monogenic disorders (PGT-M). METHODS: A patient with craniofacial deformities and his parents who had visited Shiyan People's Hospital in June 2020 were selected as the study subjects. The child underwent whole exome sequencing (WES) and qPCR validation. After genetic counseling, PGT-M was chosen for the reproductive blockage. This study was approved by the Ethics Committee of the Hospital (Ethics No.: sysrmyy-087). RESULTS: The child was diagnosed with CFNS due to a heterozygous deletion of exons 1-5 of the EFNB1 gene through WES and qPCR, which showed a X-linked dominant inheritance. The mother underwent ovarian stimulation with a modified PPOS protocol, which has yielded 11 oocytes. After ICSI fertilization, 4 blastocysts were formed, and MALBAC whole genome amplification was performed on the trophoblast biopsy cells, and SNP haplotypes of the family members and embryos were analyzed to indirectly determine the presence of maternal pathogenic haplotypes. Chromosomal copy number variation analysis was conducted through next-generation sequencing to screen for euploid embryos, resulting in the identification of two euploid embryos that did not carry the mutation of the EFNB1 gene. The first transfer was unsuccessful, but after adjusting the transfer timing through endometrial receptivity assessment (ERA), clinical pregnancy was achieved. Prenatal diagnosis at 19 weeks excluded the EFNB1 gene exons 1-5 deletion in the fetus. A healthy girl was delivered by Cesarean section at 38⁺⁶ weeks, and Q-PCR confirmed she has no aforementioned EFNB1 gene deletion. CONCLUSION This study has successfully blocked the transmission of CFNS caused by a large deletion of the EFNB1 gene (exons 1-5) using a PGT-M strategy, which may provide reference for the intervention of similar genomic variations that cannot be directly detected.
Humans ; Female ; Male ; Craniofacial Abnormalities/diagnosis* ; Ephrin-B1/genetics* ; Polymorphism, Single Nucleotide/genetics* ; Preimplantation Diagnosis/methods* ; Pedigree ; Asian People/genetics* ; Craniosynostoses/genetics* ; Pregnancy ; Gene Deletion ; Sequence Deletion ; Genetic Testing/methods* ; Adult ; East Asian People

Objective:To investigate the differences in hippocampal subfield volumes and structural covariance network properties among patients with mild cognitive impairment (MCI) exhibiting different cognitive outcomes and normal controls (NCs), and to further evaluate the predictive value of these imaging indicators for cognitive deterioration in MCI patients.Methods:A total of 43 NCs, 65 stable MCI (sMCI), and 26 progressive MCI (pMCI) patients enrolled in the Alzheimer′s Disease Neuroimaging Initiative (ADNI) database between December 2012 and May 2016 were included in this study. Baseline demographic information and T 1-weighted magnetic resonance imaging scans were collected. Hippocampal subfield volumes were extracted using freesurfer software, and structural covariance networks of hippocampal subfields were constructed. Multivariate analysis of covariance was used to compare hippocampal subfield volumes among the 3 groups. A general linear model was applied to examine group differences in hippocampal subfield structural covariance network properties. Least absolute shrinkage and selection operator (LASSO)-Logistic regression was employed to identify imaging predictors associated with conversion to Alzheimer′s disease (AD), based on which structural, network-based, and combined predictive models were constructed. Model discrimination was evaluated using the area under the curve (AUC); internal validation was performed using Bootstrap resampling; model calibration was assessed with the Hosmer-Lemeshow test; and clinical utility was evaluated through decision curve analysis. Results:Significant differences in hippocampal subfield volumes (mm3) were observed among the 3 groups (all P<0.05, Bonferroni-corrected). Specifically, left parasubiculum (65.58±13.30, 61.96±17.56, 49.56±11.82, F=9.900), right parasubiculum (65.92±15.21, 59.45±16.65, 47.69±15.48, F=11.612), left presubiculum (277.09±39.85, 258.15±44.86, 224.05±45.05, F=14.513), right presubiculum (262.85±40.43, 247.41±43.27, 209.97±46.11, F=14.500), left subiculum (399.66±32.19, 374.25±55.83, 306.12±51.62, F=32.923), right subiculum (417.93±48.92, 376.59±51.01, 316.82±70.22, F=28.764), left cornu ammonis 1 (CA1) (592.10±83.87, 561.96±94.72, 490.06±86.89, F=13.352), right CA1 (632.15±100.09, 601.24±88.88, 531.05±110.29, F=10.579), left CA3 (191.58±30.08, 180.47±34.66, 155.08±37.82, F=12.182), right CA3 (210.42±28.92, 203.84±34.80, 176.69±41.47, F=9.597), left CA4 (224.61±28.94, 210.49±35.04, 183.98±36.89, F=16.521), right CA4 (238.49±28.14, 227.43±30.65, 200.23±42.74, F=13.702), left granule cell-molecular layer-dentate gyrus (GC-ML-DG) (259.96±36.76, 239.42±41.17, 207.61±41.84, F=19.831), right GC-ML-DG (273.98±35.12, 258.79±36.82, 227.81±49.07, F=14.204), left molecular layer (505.62±66.16, 468.58±75.17, 402.68±75.47, F=22.293), right molecular layer (527.39±72.39, 493.14±70.39, 423.81±88.09, F=19.588), left hippocampal amygdala transition area (HATA) (54.91±9.99, 49.52±9.93, 43.27±9.59, F=13.571), right HATA (58.43±9.83, 54.55±10.80, 47.12±12.54, F=10.037), left fimbria (69.94±25.04, 56.63±23.74, 40.58±19.83, F=14.846), right fimbria (68.61±26.24, 53.95±23.16, 45.25±17.04, F=10.424), left hippocampal tail (488.37±83.44, 463.54±80.33, 393.83±77.73, F=13.570), and right hippocampal tail (519.78±80.22, 498.84±81.68, 419.75±93.29, F=14.339) all showed significant group differences. Significant group differences were also observed in small-worldness metric γ (0.51±0.10, 0.51±0.08, 0.62±0.14, F=9.317), small-worldness metric λ (0.39±0.02, 0.39±0.02, 0.43±0.04, F=9.925), global efficiency (0.19±0.01, 0.20±0.01, 0.18±0.01, F=3.189), local efficiency (0.26±0.02, 0.26±0.01, 0.27±0.01, F=3.068), clustering coefficient (0.23±0.01, 0.23±0.01, 0.24±0.02, F=4.274), and characteristic path length (0.73±0.06, 0.72±0.06, 0.76±0.07, F=4.477) of the hippocampal subfield structural covariance network (all P<0.05). Specifically, the pMCI group exhibited higher γ ( t=3.773, P<0.001), λ ( t=4.060, P<0.001), local efficiency ( t=2.445, P=0.047), and clustering coefficient ( t=2.849, P=0.015) than the NCs group, and higher γ ( t=4.074, P<0.001), λ ( t=4.068, P<0.001), and characteristic path length ( t=2.986, P=0.010) but lower global efficiency ( t=-2.444, P=0.047) than the sMCI group. The AUC of the structural, network, and combined models based on LASSO-Logistic regression was 0.837, 0.861, and 0.899, respectively. After internal validation, the corrected AUC was 0.835, 0.855, and 0.889, respectively. All models demonstrated good calibration ( P>0.05), and decision curve analysis indicated favorable clinical net benefit across models. Conclusions:Both sMCI and pMCI patients exhibit widespread hippocampal subfield atrophy and altered global properties of hippocampal subfield structural covariance networks compared to NCs. The models constructed based on hippocampal subfield volumes and structural covariance networks show strong potential for predicting cognitive decline in MCI patients.

Objective:To investigate the differences in hippocampal subfield volumes and structural covariance network properties among patients with mild cognitive impairment (MCI) exhibiting different cognitive outcomes and normal controls (NCs), and to further evaluate the predictive value of these imaging indicators for cognitive deterioration in MCI patients.Methods:A total of 43 NCs, 65 stable MCI (sMCI), and 26 progressive MCI (pMCI) patients enrolled in the Alzheimer′s Disease Neuroimaging Initiative (ADNI) database between December 2012 and May 2016 were included in this study. Baseline demographic information and T 1-weighted magnetic resonance imaging scans were collected. Hippocampal subfield volumes were extracted using freesurfer software, and structural covariance networks of hippocampal subfields were constructed. Multivariate analysis of covariance was used to compare hippocampal subfield volumes among the 3 groups. A general linear model was applied to examine group differences in hippocampal subfield structural covariance network properties. Least absolute shrinkage and selection operator (LASSO)-Logistic regression was employed to identify imaging predictors associated with conversion to Alzheimer′s disease (AD), based on which structural, network-based, and combined predictive models were constructed. Model discrimination was evaluated using the area under the curve (AUC); internal validation was performed using Bootstrap resampling; model calibration was assessed with the Hosmer-Lemeshow test; and clinical utility was evaluated through decision curve analysis. Results:Significant differences in hippocampal subfield volumes (mm3) were observed among the 3 groups (all P<0.05, Bonferroni-corrected). Specifically, left parasubiculum (65.58±13.30, 61.96±17.56, 49.56±11.82, F=9.900), right parasubiculum (65.92±15.21, 59.45±16.65, 47.69±15.48, F=11.612), left presubiculum (277.09±39.85, 258.15±44.86, 224.05±45.05, F=14.513), right presubiculum (262.85±40.43, 247.41±43.27, 209.97±46.11, F=14.500), left subiculum (399.66±32.19, 374.25±55.83, 306.12±51.62, F=32.923), right subiculum (417.93±48.92, 376.59±51.01, 316.82±70.22, F=28.764), left cornu ammonis 1 (CA1) (592.10±83.87, 561.96±94.72, 490.06±86.89, F=13.352), right CA1 (632.15±100.09, 601.24±88.88, 531.05±110.29, F=10.579), left CA3 (191.58±30.08, 180.47±34.66, 155.08±37.82, F=12.182), right CA3 (210.42±28.92, 203.84±34.80, 176.69±41.47, F=9.597), left CA4 (224.61±28.94, 210.49±35.04, 183.98±36.89, F=16.521), right CA4 (238.49±28.14, 227.43±30.65, 200.23±42.74, F=13.702), left granule cell-molecular layer-dentate gyrus (GC-ML-DG) (259.96±36.76, 239.42±41.17, 207.61±41.84, F=19.831), right GC-ML-DG (273.98±35.12, 258.79±36.82, 227.81±49.07, F=14.204), left molecular layer (505.62±66.16, 468.58±75.17, 402.68±75.47, F=22.293), right molecular layer (527.39±72.39, 493.14±70.39, 423.81±88.09, F=19.588), left hippocampal amygdala transition area (HATA) (54.91±9.99, 49.52±9.93, 43.27±9.59, F=13.571), right HATA (58.43±9.83, 54.55±10.80, 47.12±12.54, F=10.037), left fimbria (69.94±25.04, 56.63±23.74, 40.58±19.83, F=14.846), right fimbria (68.61±26.24, 53.95±23.16, 45.25±17.04, F=10.424), left hippocampal tail (488.37±83.44, 463.54±80.33, 393.83±77.73, F=13.570), and right hippocampal tail (519.78±80.22, 498.84±81.68, 419.75±93.29, F=14.339) all showed significant group differences. Significant group differences were also observed in small-worldness metric γ (0.51±0.10, 0.51±0.08, 0.62±0.14, F=9.317), small-worldness metric λ (0.39±0.02, 0.39±0.02, 0.43±0.04, F=9.925), global efficiency (0.19±0.01, 0.20±0.01, 0.18±0.01, F=3.189), local efficiency (0.26±0.02, 0.26±0.01, 0.27±0.01, F=3.068), clustering coefficient (0.23±0.01, 0.23±0.01, 0.24±0.02, F=4.274), and characteristic path length (0.73±0.06, 0.72±0.06, 0.76±0.07, F=4.477) of the hippocampal subfield structural covariance network (all P<0.05). Specifically, the pMCI group exhibited higher γ ( t=3.773, P<0.001), λ ( t=4.060, P<0.001), local efficiency ( t=2.445, P=0.047), and clustering coefficient ( t=2.849, P=0.015) than the NCs group, and higher γ ( t=4.074, P<0.001), λ ( t=4.068, P<0.001), and characteristic path length ( t=2.986, P=0.010) but lower global efficiency ( t=-2.444, P=0.047) than the sMCI group. The AUC of the structural, network, and combined models based on LASSO-Logistic regression was 0.837, 0.861, and 0.899, respectively. After internal validation, the corrected AUC was 0.835, 0.855, and 0.889, respectively. All models demonstrated good calibration ( P>0.05), and decision curve analysis indicated favorable clinical net benefit across models. Conclusions:Both sMCI and pMCI patients exhibit widespread hippocampal subfield atrophy and altered global properties of hippocampal subfield structural covariance networks compared to NCs. The models constructed based on hippocampal subfield volumes and structural covariance networks show strong potential for predicting cognitive decline in MCI patients.

During long-term electrocardiogram (ECG) monitoring, various types of noise inevitably become mixed with the signal, potentially hindering doctors' ability to accurately assess and interpret patient data. Therefore, evaluating the quality of ECG signals before conducting analysis and diagnosis is crucial. This paper addresses the limitations of existing ECG signal quality assessment methods, particularly their insufficient focus on the 12-lead multi-scale correlation. We propose a novel ECG signal quality assessment method that integrates a convolutional neural network (CNN) with a squeeze and excitation residual network (SE-ResNet). This approach not only captures both local and global features of ECG time series but also emphasizes the spatial correlation among ECG signals. Testing on a public dataset demonstrated that our method achieved an accuracy of 99.5%, sensitivity of 98.5%, and specificity of 99.6%. Compared with other methods, our technique significantly enhances the accuracy of ECG signal quality assessment by leveraging inter-lead correlation information, which is expected to advance the development of intelligent ECG monitoring and diagnostic technology.
Electrocardiography/methods* ; Humans ; Signal Processing, Computer-Assisted ; Neural Networks, Computer ; Algorithms

OBJECTIVE To investigate the intervention effect and potential mechanism of breviscapine on hepatic fibrosis （HF） in rats based on the transforming growth factor-β（1 TGF-β1）/Smad2/extracellular signal-regulated protein kinase 1（ERK1） and Kelch-like epichlorohydrin-associated protein 1（Keap1）/nuclear factor-erythroid 2-related factor 2（Nrf2）/heme oxygenase-1（HO-1） pathways. METHODS Totally 60 rats were randomly divided into normal control group， model group， breviscapine low-dose， medium-dose and high-dose groups （5.4， 10.8， 21.6 mg/kg）， and colchicine group （positive control， 0.45 mg/kg）， with 10 rats in each group， half male and half female. Except for the normal control group， HF model of the other groups was induced by carbon tetrachloride. Subsequently， each drug group was given corresponding medicine by gavage once a day for 28 days. The liver appearance of rats in each group was observed and their liver coefficients were calculated. The levels of alanineaminotransferase （ALT） and aspartate aminotransferase （AST）in serum， those of ALT， AST， superoxide dismutase （SOD），malondialdehyde （MDA） and glutathione peroxidase （GSH- Px） in liver tissue were detected. The liver tissue inflammatory and fibrotic changes were observed. The protein and mRNA expressions of TGF-β1， Smad2， ERK1， Nrf2， Keap1 and HO-in liver tissue were detected. RESULTS Compared with the normal control group， the model group showed large areas of white nodular lesions in the liver， obvious inflammatory cell infiltration and collagen fiber deposition. The body weight， the levels of SOD and GSH-Px in liver tissue， the protein and mRNA expressions of Nrf2 and HO-1 were significantly lowered in the model group （P＜0.05）； the liver coefficient， the percentage of Masson staining positive area， ALT and AST levels of serum and liver tissue， MDA level of liver tissue， the protein and mRNA expressions of TGF-β1， Smad2， ERK1 and Keap1 were significantly increased （P＜0.05）. Compared with the model group， the liver lesions of rats in each drug group were improved， and the above quantitative indexes were generally reversed （P＜0.05）. CONCLUSIONS Breviscapine has a good intervention effect on HF rats， which may be related to inhibiting TGF-β1/Smad2/ERK1 pathway for anti-fibrosis and regulating Keap1/Nrf2/HO-1 pathway to inhibit oxidative stress.

OBJECTIVE To establish the project approval evaluation system for traditional Chinese medicine （TCM） preparations in medical institutions guided by new drug conversion， to improve the success rate of approval for TCM preparations in medical institutions and lay the foundation for the later drug conversion. METHODS Research and development team used the literature research method and brainstorming method to list and organize relevant elements of project evaluation and determine the initial indicator system. Experts were consulted using the Delphi method to confirm the evaluation index. The weights were calculated based on the proportion of importance scores for each indicator and assigned specific scores to each item. The indicator system was used to evaluate 31 TCM preparations applied for filing by various departments of our hospital from April to July 2023. RESULTS After two rounds of 17 experts’ consultation， the final TCM preparation system included five primary indicators， i.e. theoretical basis， clinical research foundation， pharmaceutical foundation， prescription， and clinical value， as well as 17 secondary indicators including prescription source， traditional Chinese medicine theory， clinical positioning and so on. Human experience was considered as the item which would be rejected as one vote. Based on the above indicator system， our hospital further improved the filing and project approval process for TCM preparations in medical institutions. Among the 31 TCM preparations applied for filing by various departments from April to July 2023， 8 TCM preparations with a score ≥65 were selected for development. CONCLUSIONS The evaluation system is objective， comprehensive， and highly operable. It is suitable for the selection of TCM preparations in medical institutions before research and development.

Photoplethysmography (PPG) is often affected by interference, which could lead to incorrect judgment of physiological information. Therefore, performing a quality assessment before extracting physiological information is crucial. This paper proposed a new PPG signal quality assessment by fusing multi-class features with multi-scale series information to address the problems of traditional machine learning methods with low accuracy and deep learning methods requiring a large number of samples for training. The multi-class features were extracted to reduce the dependence on the number of samples, and the multi-scale series information was extracted by a multi-scale convolutional neural network and bidirectional long short-term memory to improve the accuracy. The proposed method obtained the highest accuracy of 94.21%. It showed the best performance in all sensitivity, specificity, precision, and F1-score metrics, compared with 6 quality assessment methods on 14 700 samples from 7 experiments. This paper provides a new method for quality assessment in small samples of PPG signals and quality information mining, which is expected to be used for accurate extraction and monitoring of clinical and daily PPG physiological information.
Photoplethysmography ; Machine Learning ; Neural Networks, Computer

Objective:To formulate surgical strategies and guide the implementation of laparoscopic anatomical hepatectomy with preoperative simulative resection.Methods:Twenty-two cases of hepatocellular carcinoma undergoing laparoscopic lobe, segment, subsegment and combined segment liver resection following preoperative simulative resection from Sep 2020 to Jan 2022 were enrolled in this study retrospectively.We observed and analyzed the operation time,intraoperative blood loss,postoperative hospital stay and postoperative complication.Results:All patients underwent laparoscopic hepatectomy successfully according to the preoperative simulative resection plan without conversion, some of them adjusted plan according to preoperative simulative resection. The median operation time was 170.0 min, the median intraoperative blood loss was 150.0 ml, the median times of pringle maneuver was done on 4 episodes, and the median postoperative hospital stay was 6.5 days. There were no severe postoperative complications in all cases.Conclusion:Preoperative simulative resection can plan the range of surgical resection accurately by visualizing important anatomical structures,greatly helping the actual surgical hepatectomy.

The extraction of pulse rate variability(PRV) in daily life is often affected by exercise and blood perfusion. Therefore, this paper proposes a method of detecting pulse signal and extracting PRV in post-ear, which could improve the accuracy and stability of PRV in daily life. First, the post-ear pulse signal detection system suitable for daily use was developed, which can transmit data to an Android phone by Bluetooth for daily PRV extraction. Then, according to the state of daily life, nine experiments were designed under the situation of static, motion, chewing, and talking states, respectively. Based on the results of these experiments, synchronous data acquisition of the single-lead electrocardiogram (ECG) signal and the pulse signal collected by the commercial pulse sensor on the finger were compared with the post-auricular pulse signal. According to the results of signal wave, amplitude and frequency-amplitude characteristic, the post-ear pulse signal was significantly steady and had more information than finger pulse signal in the traditional way. The PRV extracted from post-ear pulse signal has high accuracy, and the accuracy of the nine experiments is higher than 98.000%. The method of PRV extraction from post-ear has the characteristics of high accuracy, good stability and easy use in daily life, which can provide new ideas and ways for accurate extraction of PRV under unsupervised conditions.
Ear ; Electrocardiography, Ambulatory ; Fingers ; Heart Rate ; Humans ; Monitoring, Ambulatory ; Motion ; Pulse

Objective To establish a quick and accurate method for detection of tree shrew adenovirus(TAV) using TaqMan real-time fluorescence quantitative PCR. Methods Based on the published TAV genome sequence, a 3' conserved sequence was used to design specific probe primers. A standard curve was prepared using a recombinant plasmid containing the target gene fragment. A real-time fluorescence quantitative PCR method was established for detecting TAV based on TaqMan probe. Results The detection method was specific and was not cross-reactive with other common pathogens. The detection limit of the method was 3.7 copies/μL,showing a high sensitivity. The correlation coefficient was 0.998, and the efficiency was 95.7%. The amplification result showed a fine linear relationship,and the repeatability test effect was good. Conclusions The TAV real-time quantitative PCR detection method based on TaqMan probe has been successfully established. It has high sensitivity and reproducibility and can be applied to early detection of TAV infection.