A detection model based on Gramian angular field(GAF)and parallel KConvNeXt network is proposed for accurately detecting the abnormal conditions caused by sample needle blockage in the sampling system during the sampling,thus improving the testing accuracy and detection efficiency of automated biochemical analyzers.GAF-based method is employed to transform the time series of one-dimensional pressure signals into two-dimensional image representations.Subsequently,an improved attention mechanism integrated with a parallel dual-channel KConvNeXt network is used to classify the pressure signals,and achieves a final classification accuracy of 94.58%.The experimental results show that the proposed method can effectively capture the key characteristics of the pressure signals,offering an efficient solution for the anomalous pressure detection in biochemical analyzer sampling system and exhibiting important practical significance.

A detection model based on Gramian angular field(GAF)and parallel KConvNeXt network is proposed for accurately detecting the abnormal conditions caused by sample needle blockage in the sampling system during the sampling,thus improving the testing accuracy and detection efficiency of automated biochemical analyzers.GAF-based method is employed to transform the time series of one-dimensional pressure signals into two-dimensional image representations.Subsequently,an improved attention mechanism integrated with a parallel dual-channel KConvNeXt network is used to classify the pressure signals,and achieves a final classification accuracy of 94.58%.The experimental results show that the proposed method can effectively capture the key characteristics of the pressure signals,offering an efficient solution for the anomalous pressure detection in biochemical analyzer sampling system and exhibiting important practical significance.

Objective:To evaluate the value of dual-layer spectral detector CT (DLSDCT) in precise radiotherapy for central lung cancer (CLC) complicated with atelectasis.Methods:Clinical and imaging data (including DLSDCT, PET-CT, and radiotherapy simulation CT images) of 26 patients with pathologically confirmed CLC accompanied by atelectasis from the Third Affiliated Hospital of Shandong First Medical University and Shandong Cancer Hospital were analyzed retrospectively. There were 21 males and 5 females, aged 36-82 years. Two physicians assessed CLC identifiability on DLSDCT, PET-CT, and simulation localization CT images, respectively, and outlined the gross tumor volume (GTV) and measured GTV values (GTV DLSDCT, GTV PET-CT, GTV CT). Paired-sample Friedman test was used to compare the differences in GTV of the three images, and the SNK test with Bonferroni correction was used for a two-way comparison. The intra-class correlation coefficient (ICC) was used to compare the agreement of measured GTV between 2 physicians. Results:The differentiation rates on PET-CT, DLSDCT, and simulation CT images were 100% (26/26), 80.77% (21/26), and 11.54% (3/26), respectively. The differentiation rate of CLC on DLSDCT images was significantly higher than that on simulation CT images (χ 2=16.06, P<0.001). GTV CT, GTV PET-CT, and GTV DLSDCT measured on simulation localization CT images, PET-CT images, and DLSDCT images were 58.75 (22.57, 86.17) cm 3, 47.34 (18.13, 69.25) cm 3, and 51.40 (18.87, 71.31) cm 3, respectively, with statistically significant differences (χ 2=44.99, P<0.001). Both GTV DLSDCT and GTV PET-CT were significantly smaller than GTV CT (χ 2=4.23, 6.59, Bonferroni corrected P<0.001), and there was no significant difference between GTV DLSDCT and GTV PET-CT (χ 2=2.36, Bonferroni corrected P=0.055). The agreement between the two physicians was good for GTV values measured on both DLSDCT and PET-CT (ICC=0.86, 0.89). Conclusions:On DLSDCT images, most CLC and atelectasis can be identified. Compared to simulation localization CT, the tumor target areas outlined on DLSDCT are closer to PET-CT, and the tumor volumes outlined by different physicians are more consistent.

Objective:To evaluate the application value of double-layer spectral detector CT (DLSDCT) in differentiating central lung cancer from atelectasis.Methods:Fifty-one patients with central lung cancer accompanied by atelectasis confirmed by pathology in Shandong Cancer Hospital from January to December 2020 were enrolled. The differentiating rates of tumor from atelectasis of the conventional CT, 40 keV virtual monoenergetic imaging (MonoE), iodine density map (ID), and 40 keV MonoE-ID fusion images of unenhanced scan, arterial and venous phase were retrospectively analyzed. Cochran′s Q test was used to compare the differentiating rate (Bonferroni correction afterwards), and Friedman test was used to compare the maximum diameter of the tumor of each imaging method.Results:Of the 51 patients, none of the conventional CT images, 40 keV MonoE, ID and 40 keV MonoE-ID fusion images in the unenhanced scan could distinguish lung tumors from atelectasis; the number of cases in which the conventional CT images, 40 keV MonoE, ID and 40 keV MonoE-ID fusion images in the arterial phase could distinguish lung tumors from atelectasis regions was 4, 5, 5 and 6, respectively; the number of cases in which the conventional CT, 40 keV MonoE, ID and 40 keV MonoE-ID fusion images in the venous phase could distinguish lung tumors from atelectasis regions was 17, 35, 39 and 38, respectively, and the difference was statistically significant (χ 2=52.40, P<0.001). The differentiating rates of the 40 keV MonoE, ID, and 40 keV MonoE-ID fusion image were significantly higher than that of conventional CT image (χ2=-0.35, -0.43, -0.41, P<0.001, Bonferroni correction afterwards); There was no significant difference in the differentiating rate among 40 keV MonoE, ID and MonoE-ID fusion image ( P=1.00). Seventeen tumors could be differentiated in conventional CT images, 40 keV MonoE, ID and 40 keV MonoE-ID fusion images of venous phase, and the maximum diameters of lung tumors were (50±21) mm, (50±23) mm, (50±23) mm, and (50±23) mm, respectively, and the difference was not statistically significant (χ2=3.61, P=0.31). Conclusions:The venous phase spectral images of DLSDCT can differentiate most central lung cancer from atelectasis. Therefore, it has great clinical application value in tumor staging, efficacy evaluation, and radiotherapy.

OBJECTIVETo observe the effect difference betweendecoction combined with acupuncture at back-points and simpledecoction for chronic fatigue syndrome.

METHODSSixty patients were randomly assigned into an herbal group and a combination group, 30 cases in each one. Simpledecoction was used in the herbal group for continuous one month, one decoction a day. Based on that in the herbal group, 30 min acupuncture was used in the combination group at bilateral Xinshu (BL 15), Feishu (BL 13), Pishu (BL 20), Ganshu (BL 18) and Shenshu (BL 23), with acupoints according to syndrome differentiation. Acupuncture was given for 3 courses, 10 times as a course with 3 days between two courses, once a day. Fatigue status was evaluated before and after treatment by fatigue scale 14 (FS-14) and self-rating anxiety scale (SAS).

RESULTSThe FS-14 scores, including body fatigue scores, mental fatigue scores and total scores, and SAS scores after treatment were lower than those before treatment in the two groups (all<0.01), with better improvements in the combination group (all<0.01).

CONCLUSION decoction combined with acupuncture at back-points can improve chronic fatigue syndrome, which are better than simpledecoction.

Objective To analyze retrospectively the HRCT signs in the patients with invasive pulmonary aspergillosis and evaluate the value of HRCT in the diagnosis of invasive pulmonary aspergillosis.Methods The cilinical and HRCT images of 30 cases with invasive pulmonary aspergillosis diagnosed by fiber bronchoscopy, CT guided biopsy or sputum culture were collected.HRCT images were analyzed and the HRCT signs were summarized by two experienced chest imaging radiologists.Results 19 patients had a variety of CT signs, the sign of tree in bud was seen in 8 cases, bronchial stenosis 6 cases, bronchiectasis 8 cases, ground-glass opacity 8 cases, acinic nodules 10 cases, nodular lesions 12 cases, acinar nodules with halo sign 4 cases, nodules with halo sign 9 cases, cavity 10 cases.11 cases only had a single CT sign, the sign of tree in bud was seen in 2 cases, bronchiectasis 2 cases, ground-glass opacity 1 case, acinar nodules 2 cases, nodules with halo sign 2 cases, cavity 2 cases.The occurrence rates of various signs in 30 cases were as follows, the sign of tree in bud was 33.3%, bronchial stenosis 20%, bronchiectasis 33.3%, ground-glass opacity 30%, acinar nodule 40%, nodular lesion 46.6%, halo sign 53.3%,cavity 40%.Conclusion The main HRCT signs in the patients with invasive pulmonary aspergillosis includes tree in bud, bronchial stenosis, bronchiectasis, ground-glass opacity, acinar nodules, nodal lesions,pulmonary cavity and halo sign.The signs of bronchiectasis with tree in bud sign, acinar nodule and halo sign in the HRCT images are highly specific in the diagnosis of invasive pulmonary aspergillosis.