[Revision rate of periprosthetic joint infection post total hip or knee arthroplasty of 34 hospitals in China between 2015 and 2017: a multi-center survey]

Objective: To investigate the rate of periprosthetic joint infection (PJI) revision surgeries and clinical information of hip-/knee- PJI cases nationwide from 2015 to 2017 in China. Methods: An epidemiological investigation. A self-designed questionnaire and convenience sampling were used to survey 41 regional joint replacement centers nationwide from November 2018 to December 2019 in China. The PJI was diagnosed according to the Musculoskeletal Infection Association criteria. Data of PJI patients were obtained by searching the inpatient database of each hospital. Questionnaire entries were extracted from the clinical records by specialist. Then the differences in rate of PJI revision surgery between hip- and knee- PJI revision cases were calculated and compared. Results: Total of 36 hospitals (87.8%) nationwide reported data on 99 791 hip and knee arthroplasties performed from 2015 to 2017, with 946 revisions due to PJI (0.96%). The overall hip-PJI revision rate was 0.99% (481/48 574), and it was 0.97% (135/13 963), 0.97% (153/15 730) and 1.07% (193/17 881) in of 2015, 2016, 2017, respectively. The overall knee-PJI revision rate was 0.91% (465/51 271), and it was 0.90% (131/14 650), 0.88% (155/17 693) and 0.94% (179/18 982) in 2015, 2016, 2017, respectively. Heilongjiang (2.2%, 40/1 805), Fujian (2.2%, 45/2 017), Jiangsu (2.1%, 85/3 899), Gansu (2.1%, 29/1 377), Chongqing (1.8%, 64/3 523) reported relatively high revision rates. Conclusions: The overall PJI revision rate in 34 hospitals nationwide from 2015 to 2017 is 0.96%. The hip-PJI revision rate is slightly higher than that in the knee-PJI. There are differences in revision rates among hospitals in different regions.

Identification of molecular subtypes and prognostic signatures based on transient receptor potential channel-related genes to predict the prognostic risk of hepatocellular carcinoma: A review

Abnormal transient receptor potential (TRP) channel function interferes with intracellular calcium-based signaling and causes malignant phenotypes. However, the effects of TRP channel-related genes on hepatocellular carcinoma (HCC) remain unclear. This study aimed to identify HCC molecular subtypes and prognostic signatures based on TRP channel-related genes to predict prognostic risks. Unsupervised hierarchical clustering was applied to identify HCC molecular subtypes using the expression data of TRP channel-related genes. This was followed by a comparison of the clinical and immune microenvironment characteristics between the resulting subtypes. After screening for differentially expressed genes among subtypes, prognostic signatures were identified to construct risk score-based prognostic and nomogram models and predict HCC survival. Finally, tumor drug sensitivities were predicted and compared between the risk groups. Sixteen TRP channel-related genes that were differentially expressed between HCC and non-tumorous tissues were used to identify 2 subtypes. Cluster 1 had higher TRP scores, better survival status, and lower levels of clinical malignancy. Immune-related analyses also revealed higher infiltration of M1 macrophages and higher immune and stromal scores in Cluster 1 than in Cluster 2. After screening differentially expressed genes between subtypes, 6 prognostic signatures were identified to construct prognostic and nomogram models. The potential of these models to assess the prognostic risk of HCC was further validated. Furthermore, Cluster 1 was more distributed in the low-risk group, with higher drug sensitivities. Two HCC subtypes were identified, of which Cluster 1 was associated with a favorable prognosis. Prognostic signatures related to TRP channel genes and molecular subtypes can be used to predict HCC risk.

[Optimized pathway to schistosomiasis elimination in China: a scrutiny using a marginal benefit approach]

Following concerted efforts for over 7 decades, great achievements have been gained in the national schistosomiasis control program of China. Currently, China is moving towards the stage of schistosomiasis elimination, when the major task is to make full use of available resources to improve schistosomiasis surveillance and response to sustainably consolidate gained schistosomiasis control achievements and prevent re-emerging schistosomiasis. There is therefore an urgent need for optimization of interventions for schistosomiasis elimination. Based on analysis of socioeconomic features at different stages of the national schistosomiasis control program in China, this review discusses the relationship between the needs of assessment of schistosomiasis elimination interventions and the optimized strategy of schistosomiasis elimination at different stages of the national schistosomiasis control program using a marginal benefit approach and proposes the optimized schistosomiasis elimination strategy that allows the highest marginal benefit with currently available schistosomiasis elimination costs, so as to provide the optimal strategic pathway to schistosomiasis elimination and facilitate the achievement of the targets set in Healthy China 2030.

Deep learning for predicting the risk of immune checkpoint inhibitor-related pneumonitis in lung cancer

AIM

To develop and validate a nomogram model that combines computed tomography (CT)-based radiological factors extracted from deep-learning and clinical factors for the early predictions of immune checkpoint inhibitor-related pneumonitis (ICI-P).

MATERIALS AND METHODS

Forty ICI-P patients and 101 patients without ICI-P were divided randomly into the training (n=113) and test (n=28) sets. The convolution neural network (CNN) algorithm was used to extract the CT-based radiological features of predictable ICI-P and calculated the CT score of each patient. A nomogram model to predict the risk of ICI-P was developed by logistic regression.

RESULTS

CT score was calculated from five radiological features extracted by the residual neural network-50-V2 with feature pyramid networks. Four predictors of ICI-P in the nomogram model included a clinical feature (pre-existing lung diseases), two serum markers (absolute lymphocyte count and lactate dehydrogenase), and a CT score. The area under curve of the nomogram model in the training (0.910 versus 0.871 versus 0.778) and test (0.900 versus 0.856 versus 0.869) sets was better than the radiological and clinical models. The nomogram model showed good consistency and better clinical practicability.

CONCLUSION

The nomogram model that combined CT-based radiological factors and clinical factors can be used as a new non-invasive tool for the early prediction of ICI-P in lung cancer patients after immunotherapy with low cost and low manual input.

[Preparation and characterization of a recombinant poly-epitopic vaccine EgG1Y162-2 (4) against cystic echinococcosis based on the linker GSGGSG]

OBJECTIVE

To perform prokaryotic expression and preliminary characterization of the recombinant poly-epitope vaccine EgG1Y162-2 (4) against cystic echinococcosis.

METHODS

The recombinant poly-epitope vaccine EgG1Y162-2 (4) against Echinococcus granulosus based on the linker GSGGSG was subjected to structural three-dimensional (3D) modeling using immunoinformatics to analyze the structural changes and evaluate the antigenicity of the vaccine. The pET30a-EgG1Y162-2 (4) recombinant plasmid was generated using double digestion with EcoR I and Sal I, and then transformed into competent cells. Following protein induction with isopropyl-β-D-thiogalactoside (IPTG), the prokaryotic expression proteins were characterized using Western blotting, and the antigenicity of the recombinant protein was analyzed using sera from cystic echinococcosis patients and health volunteers.

RESULTS

The four EgG1Y162-2 proteins coupled by the 3D structure of the recombinant vaccine EgG1Y162-2 (4) presented independent and effective expression and good antigenicity. The highest protein expression was detected in the supernatant following induction of the recombinant plasmid pET30a-EgG1Y162-2 (4) by 0.2 mmol/L IPTG at 37 °C for 4 h, and a pure protein component was seen following elution with 60 mmol/L imidazole. Western blotting analysis of the recombinant multiepitope protein HIS-EgG1Y162-2 (4) showed a band at approximately 39 kDa, and this band was recognized by sera from cystic echinococcosis patients.

CONCLUSIONS

A recombinant poly-epitope vaccine EgG1Y162-2 (4) against cystic echinococcosis has been successfully constructed, which provides a preliminary basis for researches on recombinant multi-epitope vaccine against cystic echinococcosis.

Detection of SARS-CoV-2 VOC-Omicron using commercial sample-to-answer real-time RT-PCR platforms and melting curve-based SNP assays

Objectives

The World Health Organization (WHO) had designated the SARS-CoV-2 lineage B.1.1.529 as the new Variant of Concern Omicron (VOC-Omicron) on 26^th November 2021¹. Real-time reverse transcription polymerase chain reaction (RT-PCR), single nucleotide polymorphisms (SNP) and whole genome sequencing (WGS) tests were widely employed to detect SARS-CoV-2 and its variant. Yet, the SARS-CoV-2 Omicron detection performance of commercial real-time RT-PCR platforms and SARS-CoV-2 spike SNP assays remain to be elucidated.

Methods

In the first part of this study, we evaluated the VOC-Omicron detection performance of three commercial RT-PCR sample-to-answer platforms i.e. Roche cobas® 6800/8800, Roche cobas® Liat®, and Cepheid GeneXpert® systems. The detection performances were compared to one commercial conventional real-time RT-PCR assay (TIB MOLBIOL LightMix Modular SARS and Wuhan CoV E-gene) and one in-house real-time RT-PCR assay targeting RNA-dependent RNA polymerase (RdRP) gene of SARS-CoV-2 in the WHO COVID-19 Reference Laboratory at Public Health Laboratory Services Branch, Centre for Health Protection, Department of Health, The Government of the Hong Kong Special Administrative Region. In the second part of this study, we evaluated the SNP detection performance of four TIB MOLBIOL melting curve-based assays (1. Spike S371L/S373P, 2. Spike E484A, 3. Spike E484K and 4. Spike N501Y) in clinical samples obtained from hospitalized COVID-19 patients in Hong Kong. The SNP results were compared to whole genome sequences generated by Illumina platform.

Results

The VOC-Omicron detection limits of three commercial sample-to-answer assays were tested to be ≤ 2.35 Log₁₀ dC/ml. The detection performances of the sample-to-answer platforms were comparable to the two tested conventional real-time RT-PCR assays. The test sensitivities of TIB MOLBIOL VirSNiP SARS-CoV-2 Spike S371L/S373P assay and the Spike E484A assays were 100% and 96.6% respectively and the test specificities of both assays were 100%. An aberrant melting peak at Tm 42-44°C was observed when the specimens with Omicron variant were tested with the TIB MOLBIOL VirSNiP SARS-CoV-2 Spike E484K assay. Notably, the TIB MOLBIOL VirSNiP SARS-CoV-2 Spike N501Y assay failed to detect the spike N501Y mutation of Omicron variant in the tested specimens.

Conclusions

The SARS-CoV-2 detection sensitivity of three commercial platforms, Roche cobas® 6800/8800, Roche cobas® Liat®, and Cepheid GeneXpert® systems were shown not to be impacted by the large number of mutations of VOC-Omicron. Also, the signature mutations i.e. Spike S371L/Spike S373P and Spike E484A in VOC-Omicron were correctly identified by the TIB MOLBIOL VirSNiP SARS-CoV-2 Spike S371L/S373P and VirSNiP SARS-CoV-2 Spike E484A assays. Unexpected findings including a shifted melting peak or absence of amplification curve/melting peak were observed when specimens with Omicron variant were tested with the TIB MOLBIOL VirSNiP SARS-CoV-2 Spike E484K assay and Spike N501Y assay, suggesting a potential alert for Omicron variant, prior confirmation by whole genome sequencing.

Structural visualization of transient interactions between the cis-acting acyltransferase and acyl carrier protein of the salinomycin modular polyketide synthase

Transient protein–protein interactions between cis-acting acyltransferase (AT) and acyl carrier protein (ACP) domains are critical for the catalysis and processivity of modular polyketide synthases (mPKSs), but are challenging for structural characterization due to the intrinsically weak binding affinity. Here, a stable complex of cis-acting AT and ACP domains from the ninth module of the salinomycin mPKS was obtained using a maleimide cross-linker and the structure of the complex was determined at 2.6 Å resolution. The crystal structure shows that the AT in combination with the ketosynthase (KS)-to-AT linker forms a C-shaped architecture to embrace the ACP. The large hydrolase subdomain of the AT serves as a major binding platform for the ACP, while the small ferredoxin-like subdomain of the AT and the KS-to-AT linker cooperate with each other to constrain binding of the ACP. The importance of interface residues in cis-acting AT–ACP interactions was confirmed by mutagenesis assays. The interaction mode observed in the cis-acting AT–ACP complex is completely different from those observed in trans-acting AT–ACP complexes, where the ACP primarily contacts the small domain of the AT. The complex structure provides detailed mechanistic insights into AT–ACP recognition in cis-AT mPKSs.

[Research advances on the techniques for diagnosing burn wound depth]

The accurate diagnosis of burn wound depth is particularly important for evaluating the disease prognosis of burn patients. In the past, the diagnosis of burn wound depth often relied on the subjective judgment of doctors. With the continuous development of diagnostic technology, the methods for judging the depth of burn wound have also been updated. This paper mainly summarizes the research progress in the applications of indocyanine green angiography, laser Doppler imaging, laser speckle contrast imaging, and artificial intelligence in the diagnosis of burn wound depth, and compares the advantages and disadvantages of these techniques, so as to provide ideas for accurate diagnosis of burn wound depth.

An angular-resolved scattered-light diagnostic for laser-plasma instability studies

We developed an angular-resolved scattered-light diagnostic station (ARSDS) to extend the study of laser-plasma instabilities (LPIs) by simultaneously diagnosing their features at different angles in a single shot. The ARSDS angularly samples the scattered light using an array of fibers with flexible setups. The collected light is detected with an imaging spectrometer, a streaked spectrometer, or a fiber-optic spectrometer to provide time-integrated/time-resolved spectral information. The ARSDS was implemented at Shenguang-II Upgrade laser facility for the double-cone ignition campaigns. Preliminary results confirm the importance of an angular-resolved detection due to the angular dependence of LPI processes, such as stimulated Raman scattering.

[Utilizing modern tissue typing technologies to promote precision kidney transplantation]

Improving quality is the primary goal of the kidney transplantation community in China. Promoting new technologies for tissue typing, including adopting high-resolution human leucocyte antigen (HLA) typing methods to ensure the integrity and uniqueness of the donor and recipient HLA genotype, combining modified cytological method and purified single antigen technology to improve the accuracy of HLA antibody detection, etc., can assess the donor-recipient's mismatch load and the immune risk of recipients more precisely, so as to guide the scientific allocation of organs and individualized immunosuppressive regime, which can contribute to the long-term survival of recipients. This is the embodiment of the core essence of"precision medicine"in the field of kidney transplantation.

Use of real-time artificial intelligence in detection of abnormal image patterns in standard sonographic reference planes in screening for fetal intracranial malformations

OBJECTIVES

To develop and validate an artificial intelligence system, the Prenatal ultrasound diagnosis Artificial Intelligence Conduct System (PAICS), to detect different patterns of fetal intracranial abnormality in standard sonographic reference planes for screening for congenital central nervous system (CNS) malformations.

METHODS

Neurosonographic images from normal fetuses and fetuses with CNS malformations at 18-40 gestational weeks were retrieved from the databases of two tertiary hospitals in China and assigned randomly (ratio, 8:1:1) to training, fine-tuning and internal validation datasets to develop and evaluate the PAICS. The system was built based on a real-time convolutional neural network (CNN) algorithm, You Only Look Once, version 3 (YOLOv3). An image dataset from a third tertiary hospital was used to further validate, externally, the performance of the PAICS and to compare its performance with that of sonologists with different levels of expertise. Furthermore, a prospective video dataset was employed to evaluate the performance of the PAICS in a real-time scan scenario. The diagnostic accuracy, sensitivity, specificity and area under the receiver-operating-characteristics curve (AUC) were calculated to assess the performance of the PAICS and to compare this with the performance of sonologists with different levels of experience.

RESULTS

In total, 43 890 images from 16 297 pregnancies and 169 videos from 166 pregnancies were used to develop and validate the PAICS. The system achieved excellent performance in identifying 10 types of intracranial image pattern, with macro- and microaverage AUCs, respectively, of 0.933 (95% CI, 0.798-1.000) and 0.977 (95% CI, 0.970-0.985) for the internal validation image dataset, 0.902 (95% CI, 0.816-0.989) and 0.898 (95% CI, 0.885-0.911) for the external validation image dataset and 0.969 (95% CI, 0.886-1.000) and 0.981 (95% CI, 0.974-0.988) in the real-time scan setting. The performance of the PAICS was comparable to that of expert sonologists in terms of macro- and microaverage accuracy (P = 0.863 and P = 0.775, respectively), sensitivity (P = 0.883, P = 0.846) and AUC (P = 0.891, P = 0.788), but required significantly less time (0.025 s per image for PAICS vs 4.4 s for experts, P < 0.001).

CONCLUSIONS

Both in the image dataset and in the real-time scan setting, the PAICS achieved excellent diagnostic performance for various fetal CNS abnormalities. Its performance was comparable to that of experts, but it required less time. A CNN algorithm can be trained to detect fetal CNS abnormalities. The PAICS has the potential to be an effective and efficient tool in screening for fetal CNS malformations in clinical practice. © 2021 International Society of Ultrasound in Obstetrics and Gynecology.