OBJECTIVE To optimize the drug placement layout， reduce pharmacists’ dispensing time， and improve the work efficiency of the inpatient pharmacy. METHODS An ABC-CVA classification method was constructed. The ABC classification was first employed to categorize drugs into three groups [A （high frequency)， B （medium frequency）， and C （low frequency）] based on drug usage frequency. Subsequently， the CVA （Critical Value Analysis） method was applied to further classify drugs with low frequency （mainly category C） but requiring urgent use （e.g.， emergency drugs） or occupying large space. RESULTS After optimizing the drug placement layout based on this method， the average daily dispensing time for long-term prescriptions across three major wards （internal medicine， surgery， and intensive care unit） was shortened by （14.7±4.9） min. The optimized average daily dispensing time in each ward was significantly lower than that before optimization， and all differences were statistically significant （ P ＜0.001）. CONCLUSIONS The established ABC-CVA classification method can precisely facilitate the optimization of drug layout in inpatient pharmacies， significantly improve pharmacists’ dispensing efficiency， and enhance the service quality of inpatient pharmacies.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To explore the value of improved ResNet18 lightweight deep learning(DL)models for automatically detecting gouty arthritis(GA)based on ultrasonogram of the first metatarsophalangeal joint(MTP1).Methods A total of 2 401 ultrasonograms obtained from 260 patients with suspected gout who underwent MTP1 ultrasound examination were included and divided into training set(1 910 ultrasonograms from 209 cases)and test set(491 ultrasonograms from 51 cases)at the ratio of 4∶1.GA lesions on ultrasonograms were manually labeled.After preprocessing,ResNet18 lightweight network was used to construct DL models for identifying the ultrasonogram category was normal or abnormal(with any manifestation of GA).Five-fold cross-validation method was adopted to evaluate the efficacy of the DL models constructed with 2,3,4 or 6 residual blocks,i.e.model 1,2,3 and 4,respectively,and the computational cost and the amount of parameters of each model were recorded.The efficacy of the models were verified using test set,and the best DL model was screened.Results The computational cost of model 1,2,3 and 4 was 7 558.27,2 963.73,4 012.33 and 6 093.39 M,respectively,while the amount of parameters was 4.61,4.91,4.91 and 5.28 M,respectively.Model 2 had the least computational cost with parameters only slightly more than model 1.In test set,no significant difference of accuracy nor the area under the curve was found among 4 models(all P＞0.05).The sensitivity of model 2 was higher than that of model 3,while its specificity was lower only than that of model 3(both P＜0.05),hence model 2 was the best DL model.Conclusion Improved ResNet18 lightweight DL models could be used for automatically detecting GA based on ultrasonogram of MTP1,among which model 2 was the best one.

Objective To explore the value of improved ResNet18 lightweight deep learning(DL)models for automatically detecting gouty arthritis(GA)based on ultrasonogram of the first metatarsophalangeal joint(MTP1).Methods A total of 2 401 ultrasonograms obtained from 260 patients with suspected gout who underwent MTP1 ultrasound examination were included and divided into training set(1 910 ultrasonograms from 209 cases)and test set(491 ultrasonograms from 51 cases)at the ratio of 4∶1.GA lesions on ultrasonograms were manually labeled.After preprocessing,ResNet18 lightweight network was used to construct DL models for identifying the ultrasonogram category was normal or abnormal(with any manifestation of GA).Five-fold cross-validation method was adopted to evaluate the efficacy of the DL models constructed with 2,3,4 or 6 residual blocks,i.e.model 1,2,3 and 4,respectively,and the computational cost and the amount of parameters of each model were recorded.The efficacy of the models were verified using test set,and the best DL model was screened.Results The computational cost of model 1,2,3 and 4 was 7 558.27,2 963.73,4 012.33 and 6 093.39 M,respectively,while the amount of parameters was 4.61,4.91,4.91 and 5.28 M,respectively.Model 2 had the least computational cost with parameters only slightly more than model 1.In test set,no significant difference of accuracy nor the area under the curve was found among 4 models(all P＞0.05).The sensitivity of model 2 was higher than that of model 3,while its specificity was lower only than that of model 3(both P＜0.05),hence model 2 was the best DL model.Conclusion Improved ResNet18 lightweight DL models could be used for automatically detecting GA based on ultrasonogram of MTP1,among which model 2 was the best one.

BACKGROUND:Due to the sudden release and the rapid removal by proteases,platelet-rich plasma hydrogel leads to shorter residence times of growth factors at the wound site.In recent years,researchers have focused on the use of hydrogels to encapsulate platelet-rich plasma in order to improve the deficiency of platelet-rich plasma hydrogels. OBJECTIVE:To prepare self-assembled polypeptide-platelet-rich plasma hydrogel and to explore its effects on the release of bioactive factors of platelet-rich plasma. METHODS:The self-assembled polypeptide was synthesized by the solid-phase synthesis method,and the solution was prepared by D-PBS.Hydrogels were prepared by mixing different volumes of polypeptide solutions with platelet-rich plasma and calcium chloride/thrombin solutions,so that the final mass fraction of polypeptides in the system was 0.1%,0.3%,and 0.5%,respectively.The hydrogel state was observed,and the release of growth factors in platelet-rich plasma was detected in vitro.The polypeptide self-assembly was stimulated by mixing 1%polypeptide solution with 1%human serum albumin solution,so that the final mass fraction of the polypeptide was 0.1%,0.3%,and 0.5%,respectively.The flow state of the liquid was observed,and the rheological mechanical properties of the self-assembled polypeptide were tested.The microstructure of polypeptide(mass fraction of 0.1%and 0.001%)-human serum albumin solution was observed by scanning electron microscope and transmission electron microscope. RESULTS AND CONCLUSION:(1)Hydrogels could be formed between different volumes of polypeptide solution and platelet-rich plasma.Compared with platelet-rich plasma hydrogels,0.1%and 0.3%polypeptide-platelet-rich plasma hydrogels could alleviate the sudden release of epidermal growth factor and vascular endothelial growth factor,and extend the release time to 48 hours.(2)After the addition of human serum albumin,the 0.1%polypeptide group still exhibited a flowing liquid,the 0.3%polypeptide group was semi-liquid,and the 0.5%polypeptide group stimulated self-assembly to form hydrogel.It was determined that human serum albumin in platelet-rich plasma could stimulate the self-assembly of polypeptides.With the increase of the mass fraction of the polypeptide,the higher the storage modulus of the self-assembled polypeptide,the easier it was to form glue.(3)Transmission electron microscopy exhibited that the polypeptide nanofibers were short and disordered before the addition of human serum albumin.After the addition of human serum albumin,the polypeptide nanofibers became significantly longer and cross-linked into bundles,forming a dense fiber network structure.Under a scanning electron microscope,the polypeptides displayed a disordered lamellar structure before adding human serum albumin.After the addition of human serum albumin,the polypeptides self-assembled into cross-linked and densely arranged porous structures.(4)In conclusion,the novel polypeptide can self-assemble triggered by platelet-rich plasma and the self-assembly effect can be accurately adjusted according to the ratio of human serum albumin to polypeptide.This polypeptide has a sustained release effect on the growth factors of platelet-rich plasma,which can be used as a new biomaterial for tissue repair.

Objective:To investigate the predictors of the efficacy of extracorporeal shock wave lithotripsy (ESWL) in the treatment of ureteral calculi, and to evaluate the predictive value of the maximum ureteral wall thickness (UWT) in the treatment of ureteral calculi with ESWL.Methods:The clinical data of 138 patients with ureteral calculi treated with ESWL in the Second People's Hospital of Hefei from January 2020 to December 2020 were retrospectively analyzed. There were 91 males and 47 females. The age was (50.9±14.8) years old. The body mass index was (25.3±3.6) kg/m 2. The stones of 73 cases were located on the left side and 65 cases were on the right side. 70 cases had upper ureteral stones, 18 cases had middle ureteral stones, and 50 cases had lower ureteral stones. The median length of the stone was 8.5 (7.5, 10.5) mm. The CT value of the stone was 509 (343, 783) HU. The anteroposterior diameter of the renal pelvis was 12.0 (10.1, 16.0) mm, and UWT was (2.8 ± 0.8) mm. All patients underwent urinary non-contrast CT before lithotripsy, and the UWT of the stone bed was measured on the CT images. According to the stone removal situation 2 weeks after the operation, the patients were divided into a successful lithotripsy group and a failed lithotripsy group. Univariate analysis was used to compare the differences of various indicators between the two groups, and multivariate logistic regression was used to analyze the independent predictors of ESWL in the treatment of ureteral calculi for the indicators. The receiver operating characteristic (ROC) curve was used to calculate the area under the curve (AUC) of each independent predictor, and the cut-off value, sensitivity and specificity were analyzed. Results:All operations were successfully completed, and the success rate of the first-stage lithotripsy was 71.7% (99/138). The results of univariate analysis showed that the stone length diameter, stone CT value, anteroposterior diameter of renal pelvis, stone skin distance, and UWT were significantly different between the successful lithotripsy group and the failure group ( P<0.05). There was no significant difference in age, gender, body mass index, stone side and stone location ( P>0.05). The results of multivariate logistic analysis showed that stone length ( OR=1.393, P=0.015), stone CT value ( OR=1.002, P=0.043) and UWT ( OR=17.997, P<0.001) were all for the efficacy of ESWL in the treatment of ureteral stones. The ROC curve was used to compare the three independent predictors. The area under the UWT curve was the largest (AUC=0.898, P<0.001), followed by the length of the stone (AUC=0.744, P<0.001), and the CT value of the stone (AUC=0.672, P= 0.002). The cut-off value for UWT was 3.19 mm, which had a sensitivity of 91.9% and a specificity of 71.8% for predicting the success of ESWL lithotripsy. When dividing the patients into thin wall group (UWT ≤3.19 mm) and thick wall group (UWT>3.19 mm) according to the cut-off value, the success rates of one-stage lithotripsy in the two groups were 89.2% (91 / 102) and 22.2% (8/36), respectively ( P<0.05). Conclusions:UWT, calculus length and calculus CT value are independent predictors of the efficacy of ESWL in the treatment of ureteral calculi, and UWT has the best predictive value. When UWT≤3.19 mm, the success rate of ESWL in the treatment of ureteral calculi is higher.

The real physical image of the affected limb, which is difficult to move in the traditional mirror training, can be realized easily by the rehabilitation robots. During this training, the affected limb is often in a passive state. However, with the gradual recovery of the movement ability, active mirror training becomes a better choice. Consequently, this paper took the self-developed shoulder joint rehabilitation robot with an adjustable structure as an experimental platform, and proposed a mirror training system completed by next four parts. First, the motion trajectory of the healthy limb was obtained by the Inertial Measurement Units (IMU). Then the variable universe fuzzy adaptive proportion differentiation (PD) control was adopted for inner loop, meanwhile, the muscle strength of the affected limb was estimated by the surface electromyography (sEMG). The compensation force for an assisted limb of outer loop was calculated. According to the experimental results, the control system can provide real-time assistance compensation according to the recovery of the affected limb, fully exert the training initiative of the affected limb, and make the affected limb achieve better rehabilitation training effect.
Electromyography ; Humans ; Movement ; Muscle Strength ; Robotics ; Shoulder Joint ; Stroke Rehabilitation

Objective: To  investigate  the  effect  of  X-ray  on  the  polarization  of  mouse  microglia  BV-2  cells. Methods: BV-2 cells at the logarithmic growth stage were randomly divided into the Sham irradiation group and 10 Gy irradiation group. The latter group was given a single X-ray irradiation at a dose of 1.28 Gy/min for 7 min 49 s. The activation rate of BV-2 cells was observed and analyzed under a microscope at 1, 3, 6, 24 h and 48 h after irradiation.The changes of cell morphology were observed by HE staining and immunofluorescence staining; The levels of M1-type activation markers (TNF-α and IL-1β) and M2-type activation marker TGF-β1 in the supernatant of BV-2 cells were detected by ELISA. The levels of polarization-related proteins of M1-type (CD86 and iNOS) and M2-type (CD206) in BV-2 cells were detected by Western blotting. Results　: Morphological results showed that BV-2 cells became larger, and their protrude became coarse and shorter, showing "amoeba" like changes after 10 Gy X-ray irradiation. Compared with the Sham group, the activation rate of BV-2 cells was significantly increased at 3 h, and reached the peak at 6 h, and began to recover at 48 h after irradiation. ELISA results showed an obvious increase in the level of TNF-α and TGF-β1 48 h after irradiation.The level of IL-1β showed a transient decrease at 3～6 h, increased at 24 h, and reached the peak 48 h after irradiation. Western blotting results showed that CD86 protein level did not change significantly at each time points after irradiation, and iNOS protein level in- creased significantly at 1, 6, 24 h and 48 h after irradiation. A fluctuating change in CD206 protein level was found after irradiation. Conclusion　 10 Gy X-ray irradiation can induce the activation of BV-2 cells in vitro, and the polarization type changes with the time after irradiation.