Deep Learning-Based Facial and Skeletal Transformations for Surgical Planning.

The increasing application of virtual surgical planning (VSP) in orthognathic surgery implies a critical need for accurate prediction of facial and skeletal shapes. The craniofacial relationship in patients with dentofacial deformities is still not understood, and transformations between facial and skeletal shapes remain a challenging task due to intricate anatomical structures and nonlinear relationships between the facial soft tissue and bones. In this study, a novel bidirectional 3-dimensional (3D) deep learning framework, named P2P-ConvGC, was developed and validated based on a large-scale data set for accurate subject-specific transformations between facial and skeletal shapes. Specifically, the 2-stage point-sampling strategy was used to generate multiple nonoverlapping point subsets to represent high-resolution facial and skeletal shapes. Facial and skeletal point subsets were separately input into the prediction system to predict the corresponding skeletal and facial point subsets via the skeletal prediction subnetwork and facial prediction subnetwork. For quantitative evaluation, the accuracy was calculated with shape errors and landmark errors between the predicted skeleton or face with corresponding ground truths. The shape error was calculated by comparing the predicted point sets with the ground truths, with P2P-ConvGC outperforming existing state-of-the-art algorithms including P2P-Net, P2P-ASNL, and P2P-Conv. The total landmark errors (Euclidean distances of craniomaxillofacial landmarks) of P2P-ConvGC in the upper skull, mandible, and facial soft tissues were 1.964 ± 0.904 mm, 2.398 ± 1.174 mm, and 2.226 ± 0.774 mm, respectively. Furthermore, the clinical feasibility of the bidirectional model was validated using a clinical cohort. The result demonstrated its prediction ability with average surface deviation errors of 0.895 ± 0.175 mm for facial prediction and 0.906 ± 0.082 mm for skeletal prediction. To conclude, our proposed model achieved good performance on the subject-specific prediction of facial and skeletal shapes and showed clinical application potential in postoperative facial prediction and VSP for orthognathic surgery.

[Construction and validation of an in-hospital mortality risk prediction model for patients receiving VA-ECMO: a retrospective multi-center case-control study].

OBJECTIVE: To investigate the risk factors of in-hospital mortality and establish a risk prediction model for patients receiving venoarterial extracorporeal membrane oxygenation (VA-ECMO). METHODS: We retrospectively collected the data of 302 patients receiving VA-ECMO in ICU of 3 hospitals in Guangdong Province between January, 2015 and January, 2022 using a convenience sampling method. The patients were divided into a derivation cohort (201 cases) and a validation cohort (101 cases). Univariate and multivariate logistic regression analyses were used to analyze the risk factors for in-hospital death of these patients, based on which a risk prediction model was established in the form of a nomogram. The receiver operator characteristic (ROC) curve, calibration curve and clinical decision curve were used to evaluate the discrimination ability, calibration and clinical validity of this model. RESULTS: The in-hospital mortality risk prediction model was established based the risk factors including hypertension (OR=3.694, 95% CI: 1.582-8.621), continuous renal replacement therapy (OR=9.661, 95%CI: 4.103-22.745), elevated Na2 + level (OR=1.048, 95% CI: 1.003-1.095) and increased hemoglobin level (OR=0.987, 95% CI: 0.977-0.998). In the derivation cohort, the area under the ROC curve (AUC) of this model was 0.829 (95% CI: 0.770-0.889), greater than those of the 4 single factors (all AUC < 0.800), APACHE II Score (AUC=0.777, 95% CI: 0.714-0.840) and the SOFA Score (AUC=0.721, 95% CI: 0.647-0.796). The results of internal validation showed that the AUC of the model was 0.774 (95% CI: 0.679-0.869), and the goodness of fit test showed a good fitting of this model (χ2=4.629, P>0.05). CONCLUSION: The risk prediction model for in-hospital mortality of patients on VA-ECMO has good differentiation, calibration and clinical effectiveness and outperforms the commonly used disease severity scoring system, and thus can be used for assessing disease severity and prognostic risk level in critically ill patients.

Saturation of Fishbone Instability by Self-Generated Zonal Flows in Tokamak Plasmas.

Gyrokinetic simulations of the fishbone instability in DIII-D tokamak plasmas find that self-generated zonal flows can dominate the nonlinear saturation by preventing coherent structures from persisting or drifting in the energetic particle phase space when the mode frequency down-chirps. Results from the simulation with zonal flows agree quantitatively, for the first time, with experimental measurements of the fishbone saturation amplitude and energetic particle transport. Moreover, the fishbone-induced zonal flows are likely responsible for the formation of an internal transport barrier that was observed after fishbone bursts in this DIII-D experiment. Finally, gyrokinetic simulations of a related ITER baseline scenario show that the fishbone induces insignificant energetic particle redistribution and may enable high performance scenarios in ITER burning plasma experiments.

[Efficacy of 0.05% cyclosporine A combined with vitamin A palmitate in the treatment of meibomian gland dysfunction-related dry eye].

Objective: To evaluate the efficacy of 0.05% cyclosporine A eye drops combined with vitamin A palmitate eye gel in the treatment of dry eye associated with meibomian gland dysfunction (MGD). Methods: A single-center, prospective, randomized, parallel controlled trial design was used to include patients diagnosed with MGD-associated dry eye. The patients were randomly divided into three groups and administered with medications binocularly for 12 weeks. The CsA+VA group was given 0.05% cyclosporine A eye drops twice a day and vitamin A palmitate eye gel three times a day. The CsA+HA group was given 0.05% cyclosporine A eye drops twice a day and 0.1% sodium hyaluronate eye drops three times a day. The HA group was given 0.1% sodium hyaluronate eye drops 3 times a day. The OSDI score, tear meniscus height, fluorescein tear break-up time, Schirmer Ⅰ test (without anesthesia), tear film lipid layer thickness, meibomian gland morphology and function examination, and corneal fluorescein sodium staining score were evaluated at baseline, 4, 8, and 12 weeks after the initiation of the treatment, respectively. Results: A total of 120 patients with MGD-related dry eye met the enrollment criteria, but 10 patients were lost to follow-up; 110 patients were finally included for observation, including 36 patients in the CsA+VA group, 38 in the CsA+HA group and 36 in the HA group. The OSDI score, tear meniscus height, fluorescein tear break-up time and meibomian gland secretion of the 3 groups were significantly improved. At the 12th week of the treatment, the differences of the CsA+VA group [25.45±15.11, (0.30±0.13) mm, (3.72±1.40) s, (5.03±2.52) points] and the CsA+HA group [26.98±16.89, (0.27±0.10) mm, (4.34±1.76) s, (5.11±2.39) points] from the HA group [24.57±11.26, (0.24±0.06) mm, (3.18±1.11) s, (9.11±3.34) points] were statistically significant (P<0.05). Compared with the CsA+HA group [(68.39±26.66) nm], the tear film lipid layer thickness in the CsA+VA group [(72.61±23.65) nm] was significantly increased (P<0.05). In the CsA+VA group, the meibomian gland secretion characters and discharge capacity among patients with severe abnormalities [(6.28±2.59) and (5.89±2.77) points at the 12th week of treatment], moderate abnormalities [(4.27±2.02) and (4.64±2.02) points at the 12th week of treatment] and mild abnormalities [(2.80±0.84) and (2.60±0.55) points at the 12th week of treatment] were significantly different (P<0.05). Conclusion: 0.05% cyclosporine A combined with vitamin A palmitate can significantly improve the symptoms and signs of patients with MGD-related dry eye, especially the tear film lipid layer thickness and the meibomian gland secretion characters and discharge capacity in severe cases.

Floating-Point Approximation Enabling Cost-Effective and High-Precision Digital Implementation of FitzHugh-Nagumo Neural Networks.

The study of neuron interactions and hardware implementations are crucial research directions in neuroscience, particularly in developing large-scale biological neural networks. The FitzHugh-Nagumo (FHN) model is a popular neuron model with highly biological plausibility, but its complexity makes it difficult to apply at scale. This paper presents a cost-saving and improved precision approximation algorithm for the digital implementation of the FHN model. By converting the computational data into floating-point numbers, the original multiplication calculations are replaced by adding the floating-point exponent part and fitting the mantissa part with piecewise linear. In the hardware implementation, shifters and adders are used, greatly reducing resource overhead. Implementing FHN neurons by this approximation calculations on FPGA reduces the normalized root mean square error (RMSE) to 3.5% of the state-of-the-art (SOTA) while maintaining a performance overhead ratio improvement of 1.09 times. Compared to implementations based on approximate multipliers, the proposed method achieves a 20% reduction in error at the cost of a 2.8% increase in overhead.This model gained additional biological properties compared to LIF while reducing the deployment scale by only 9%. Furthermore, the hardware implementation of nine coupled circular networks with eight nodes and directional diffusion was carried out to demonstrate the algorithm's effectiveness on neural networks. The error decreased to 60% compared to the single neuron of the SOTA. This hardware-friendly algorithm allows for the low-cost implementation of high-precision hardware simulation, providing a novel perspective for studying large-scale, biologically plausible neural networks.

Adapting an established Ampliseq microhaplotype panel to nanopore sequencing through direct PCR.

We have adapted an established Ampliseq microhaplotype panel for nanopore sequencing with the Oxford Nanopore Technologies (ONT) system, as a cost-effective and highly scalable solution for forensic genetics applications. For this purpose, we designed a protocol combining direct PCR amplification from unextracted DNA with ONT library construction and sequencing using the MinION device and workflow. The analysis of reference samples at input amounts of 5-10 ng of DNA demonstrates stable coverage patterns, allele balance, and strand bias, reaching profile completeness and concordance rates of ∼95%. Similar levels were achieved when using direct-PCR from blood, buccal and semen swabs. Dilution series results indicate sensitivity is maintained down to 250 pg of input DNA, and informative profiles are produced down to 62.5 pg. Finally, we demonstrated the forensic utility of the nanopore workflow by analyzing two third degree pedigrees that showed low likelihood ratio values after the analysis of an extended panel of 38 STRs, achieving likelihood ratios 2-3 orders of magnitude higher when testing with the MinION-based haplotype data.

[Clinical features of IgG4-related lung disease].

Objective: To explore the clinical features of IgG4-related lung disease. Methods: The clinical data of 60 patients diagnosed with IgG4-related lung disease in Peking University People's Hospital from February 2012 to May 2021 were retrospectively collected. Analysis was made to explore the features of clinical manifestation, laboratory, imaging, prognosis and other characteristics of the disease. Results: A total of 60 patients were included, with 40 males, age of (58.2±12.9) years, an age of onset of (57.1±13.2) years, and 31.7% (19 cases) of the patients had a history of allergic disease. 36.7% (22 cases) of the patients had respiratory symptoms during the disease. 94.6% (53/56) of patients had serum IgG4>1.35 g/L, 24.1% (14/58) of patients had increased eosinophils, 79.2% (38/48) of patients had increased IgE level, and 53.7% (29/54) of patients had decreased C3 or C4. Common imaging findings included nodular changes (38 cases, 63.3%), mediastinal and/or hilar lymphadenopathy (34 cases, 56.7%), and ground glass opacities (31 cases, 51.7%). Fifty-three cases (88.3%) showed two or more imaging changes. The pathological examination of the patient was mainly characterized by lymphoplasmacytic infiltration and fibrosis, with only one case of phlebitis obliterans. Compared with the asymptomatic group (38 cases), patients with respiratory symptoms (22 cases) showed higher level of serum total IgG and eosinophils (43.2 vs 17.8 g/L, 0.30×109/L vs 0.14×109/L, P<0.05), lower proportion of allergic diseases, and higher proportion of consolidation shadows on chest CT (P<0.05). There were no significant differences in serum IgG4, IgE, complement levels, and imaging outcomes after treatment between the two groups (P>0.05). Conclusions: The clinical manifestations of IgG4-related lung disease are atypical, and asymptomatic patients account for a high proportion. The imaging of the disease is highly heterogeneous, and patients are prone to show coexisted multiple imaging changes. The main clinical features and imaging outcomes of patients with and without respiratory symptoms are not significantly different.

[The value of machine learning models based on biparametric MRI for diagnosis of prostate cancer and clinically significant prostate cancer].

Objective: To evaluate the value of machine learning (ML) models based on biparametric magnetic resonance imaging (bpMRI) for diagnosis of prostate cancer (PCa) and clinically significant prostate cancer (csPCa). Methods: A total of 1 368 patients, aged from 30 to 92 (69.4±8.2) years, from 3 tertiary medical centers in Jiangsu Province were retrospectively collected from May 2015 to December 2020, including 412 cases of csPCa, 242 cases of clinically insignificant prostate cancer (ciPCa) and 714 cases of benign prostate lesions. The data of center 1 and center 2 were randomly divided into training cohort and internal testing cohort at a ratio of 7∶3 by random number sampling without replacement using Python Random package, and the data of center 3 were used as the independent external testing cohort. The training cohort includs 243 cases of csPCa, 135 cases of ciPCa and 384 cases of benign lesions, the internal testing cohort includs 104 cases of csPCa, 58 cases of ciPCa and 165 cases of benign lesions, and the external testing cohort includs 65 cases of csPCa, 49 cases of ciPCa and 165 cases of benign lesions. The radiomics features were extracted on T2-weighted imaging, diffusion-weighted imaging and apparent diffusion coefficient map, and optimal radiomics features were selected by using Pearson correlation coefficient method and analysis of variance. The ML models were built using two ML algorithms, including support vector machine and random forest (RF) and were further tested in the internal testing cohort and external testing cohort. Finally, the PI-RADS scores evaluated by the radiologists were adjusted by the ML models which had superior diagnostic performance, namely adjusted PI-RADS. The receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of the ML models and PI-RADS. DeLong test was used to compare the areas under curve (AUC) of models with those of PI-RADS. Results: For PCa diagnosis, in internal testing cohort, the AUC of ML model using RF algorithm and PI-RADS were 0.869 (95%CI: 0.830-0.908) and 0.874 (95%CI: 0.836-0.913), respectively, and the difference between the model and PI-RADS did not reach to the statistical significance (P=0.793). In the external testing cohort, the AUC of model and PI-RADS were 0.845 (95%CI: 0.794-0.897) and 0.915 (95%CI: 0.880-0.951), respectively, and the difference was statistically significant (P=0.01). For csPCa diagnosis, the AUC of ML model using RF algorithm and PI-RADS were 0.874 (95%CI: 0.834-0.914) and 0.892 (95%CI: 0.857-0.927), respectively, in internal testing cohort, and the difference between the model and PI-RADS was not statistically significant (P=0.341). In the external testing cohort, the AUC of model and PI-RADS were 0.876 (95%CI: 0.831-0.920) and 0.884 (95%CI: 0.841-0.926), respectively, and the difference between the model and PI-RADS was not statistically significant (P=0.704). When PI-RADS assessment was adjusted with the assistance of ML models, the specificities increased from 63.0% to 80.0% in the internal testing cohort and from 92.7% to 93.3% in the external test group in diagnosing PCa. In diagnosing csPCa, the specificities increased from 52.5% to 72.6% in the internal testing cohort and from 75.2% to 79.9% in the external testing cohort. Conclusions: The ML models based on bpMRI showed comparable diagnostic performance to PI-RADS assessed by senior radiologists and achieved good generalization ability in both diagnosing PCa and csPCa. The specificities of the PI-RADS were improved by ML models.

[The comparison of modified ESUR score and Mehralivand grade based on biparametric MRI for assessing extracapsulare extension in prostate cancer].

Objective: To compare the value of the modified European Society of Urogenital Radiology (ESUR) score and Mehralivand grade based on biparametric MRI (bpMRI) in the assessment of extracapsular extension (ECE) in patients with prostate cancer (PCa). Methods: Data of 235 patients with postoperative pathology confirmed PCa, who underwent preoperative 3.0 T bpMRI examinations between March 2019 and March 2022 in the First Affiliated Hospital of Soochow University were retrospectively evaluated, including 107 ECE positive and 128 ECE negative cases, aged [M (Q1, Q3)] [71 (66, 75)] years. Reader 1 and 2 assessed the ECE using the modified ESUR score and Mehralivand grade, and the receiver operating characteristic curve and Delong test were used to evaluate the performance of the two scoring methods. Then, the statistically significant variables were included in multivariate binary logistics regression analysis to obtain the risk factors, which were combined with the scores of reader 1 to establish combined models. The assessment ability of the two combined models and the two scoring methods were compared subsequently. Results: The AUC of Mehralivand grade in reader 1 were higher than that of the modified ESUR score in reader 1 and 2 [0.746 (95%CI: 0.685-0.800) vs 0.696 (95%CI: 0.633-0.754) and 0.691 (95%CI: 0.627-0.749), both P<0.05]. The AUC of Mehralivand grade in reader 2 was higher than that of the modified ESUR score in reader 1 and 2 [0.753 (95%CI: 0.693-0.807) vs 0.696 (95%CI: 0.633-0.754) and 0.691 (95%CI: 0.627-0.749), both P<0.05]. The AUC of the combined model 1 based on the modified ESUR score and the combined model 2 based on Mehralivand grade were higher than that in the separate modified ESUR score [0.826 (95%CI: 0.773-0.879) and 0.841 (95%CI: 0.790-0.892) vs 0.696 (95%CI: 0.633-0.754), both P<0.001], and also higher than that in the separate Mehralivand grade [0.826 (95%CI: 0.773-0.879) and 0.841 (95%CI: 0.790-0.892) vs 0.746 (95%CI: 0.685-0.800), both P<0.05]. Conclusion: Based on bpMRI, the Mehralivand grade showed better diagnostic performance for assessing ECE preoperatively in patients with PCa than the modified ESUR score. The combination model of scoring methods and clinical variables can further enhance the diagnostic certainty in the assessment of ECE.

[Analysis of clinical and pathological features of chronic hepatitis B combined with metabolic-associated fatty liver disease].

Objective: To analyze the clinical and histopathological features of patients with chronic hepatitis B (CHB) combined with metabolic-associated fatty liver disease (MAFLD). Methods: Clinical data of 529 cases who had liver biopsies at the First Affiliated Hospital of Zhengzhou University between January 2015 and October 2021 were collected. Among them were 290 cases with CHB, 155 cases with CHB combined with MAFLD, and 84 cases with MAFLD. Three groups of patients clinical data, including general information, biochemical indicators, FibroScan indicators, viral load, and histopathology, were analyzed. A binary logistic regression analysis was used to explore the factors influencing MAFLD in patients with CHB. Results: (1) Age, male status, proportion of hypertension and diabetes, body mass index, fasting blood glucose, γ-glutamyl transpeptidase, low-density lipoprotein, cholesterol, triglycerides, uric acid, creatinine, and the controlled attenuation parameter for hepatic steatosis were higher in CHB combined with MAFLD than in CHB patient groups. In contrast, the high-density lipoprotein, HBeAg positivity rate, viral load level, and liver fibrosis grade (S stage) were lower in CHB patients, and the differences were statistically significant (P < 0.05). (2) Alanine aminotransferase, aspartate aminotransferase, γ-glutamyl transpeptidase, triglycerides, uric acid, creatinine, and the controlled attenuation parameter for hepatic steatosis in CHB combined with the MAFLD were lower than those in MAFLD patient groups, while high-density lipoprotein was higher than that of MAFLD patients, and the difference was statistically significant (P < 0.05). There was no statistically significant difference in the grade of liver inflammation and fibrosis (GS stage) between the two groups (P > 0.05). Binary multivariate logistic regression analysis showed that overweight/obesity, triglycerides, low-density lipoprotein, the controlled attenuation parameter for hepatic steatosis, and HBeAg positivity were independent influencing factors for MAFLD in CHB patients. Conclusion: Patients with CHB combined with metabolic disorders are prone to developing MAFLD, and there is a certain correlation between HBV viral factors, the degree of liver fibrosis, and the fatty degeneration of hepatocytes.

[Clinical features and predictors of early neurological deterioration in acute isolated pontine infarction].

Objective: To investigate the clinical and imaging characteristics of early neurological deterioration (END) in acute isolated pontine infarction (AIPI) and analyze the predictive factors of END. Methods: Patients with AIPI who were confirmed by magnetic resonance imaging (MRI) in Zhengzhou University People's Hospital from January 2020 to December 2021were collected and divided into END group and non-END group (NEND group). General data and imaging characteristics of the patients were compared between the two groups, the neurological function of patients was evaluated by using the modified Rankin scale (mRS) at 1 and 3 months after stroke. Multivariate binary logistic regression model was used to analyze the risk factors of END after isolated pontine infarction, and the receiver operating characteristic curve(ROC) curve was drawn. Z-test was used to compare the area under the curve to determine the best predictor of END. Results: A total of 113 patients with AIPI were enrolled, including 72 males and 41 females, aged (62±11) years, with 40 cases in the END group and 73 cases in the NEND group. The incidence of END in AIPI was 35.4% (40/113). The National Institutes of Health Stroke Scale (NIHSS) score in the END group (5.15±1.88) was higher than that in the NEND group (4.10±1.63), and the infarcts size in the END group [(2.15±0.39) mm2] was larger than that in the NEND group [(1.61±0.46) mm2] (P=0.002 and P<0.001, respectively). Multivariate binary logistic regression analysis showed that NIHSS score on admission (OR=1.393, 95%CI: 1.017-1.909, P=0.039), infarct size (OR=11.539, 95%CI: 3.574-37.255, P<0.001) were associated with END. Comparing the area of ROC curve, infarct size [area under curve (AUC)=0.787, with a sensitivity of 0.750 and specificity of 0.545] and NIHSS score on admission (AUC=0.688, with a sensitivity of 0.700 and specificity of 0.589) showed no significant difference in the value of predicting END (P=0.056). Conclusion: Patients with AIPI had higher NIHSS score and larger infarct size on admission, and both of them exhibit good predictive performance for END.

Emotional responses of piglets under long-term exposure to negative and positive auditory stimuli.

The stress caused by sound is inevitable. The stress caused by noise and the positive effects of music can affect the endocrine of animals and their welfare. In this study, a total of 72 hybrid piglets (Large White × Duroc × Min pig) were randomly divided into 3 groups, including music (Mozart K.448, 60-70 dB), noise (recorded mechanical noise, 80-85 dB), and control (natural background sound, <40 dB) groups. S-IgA (secretory immunoglobulin A), IL-6 (interleukin-6), IL-8 (interleukin-8), and positive emotion-related behaviors were used as indicators to discuss whether noise induced stress and inflammation in piglets or whether music could have positive effects. Six hours of auditory exposure were given daily (10:00-16:00), which lasted for 56 days. Behavioral responses of the piglets were observed, and the concentrations of salivary S-IgA and serum IL-6 and IL-8 were measured. The results showed that the concentration of S-IgA increased in the noise and control groups on the 57th day (P < 0.05); S-IgA concentration in the music group was unchanged after long-term music exposure. The concentrations of IL-6 and IL-8 showed that long-term noise exposure might lead to stress and inflammation in piglets. Tail-wagging and play behaviors of the piglets in the music group were significantly greater than those in the noise and control groups, which implied that long-term music exposure improved the emotional state of the piglets in a restricted and barren environment.

Strong band renormalization and emergent ferromagnetism induced by electron-antiferromagnetic-magnon coupling.

The interactions between electrons and antiferromagnetic magnons (AFMMs) are important for a large class of correlated materials. For example, they are the most plausible pairing glues in high-temperature superconductors, such as cuprates and iron-based superconductors. However, unlike electron-phonon interactions (EPIs), clear-cut observations regarding how electron-AFMM interactions (EAIs) affect the band structure are still lacking. Consequently, critical information on the EAIs, such as its strength and doping dependence, remains elusive. Here we directly observe that EAIs induce a kink structure in the band dispersion of Ba1-xKxMn2As2, and subsequently unveil several key characteristics of EAIs. We found that the coupling constant of EAIs can be as large as 5.4, and it shows strong doping dependence and temperature dependence, all in stark contrast to the behaviors of EPIs. The colossal renormalization of electron bands by EAIs enhances the density of states at Fermi energy, which is likely driving the emergent ferromagnetic state in Ba1-xKxMn2As2 through a Stoner-like mechanism with mixed itinerant-local character. Our results expand the current knowledge of EAIs, which may facilitate the further understanding of many correlated materials where EAIs play a critical role.

Semen parameters and sex hormones as affected by SARS-CoV-2 infection: A systematic review.

BACKGROUND: Impaired semen quality and reproductive hormone levels were observed in patients during and after recovery from coronavirus disease 2019 (COVID-19), which raised concerns about negative effects on male fertility. Therefore, this study systematically reviews available data on semen parameters and sex hormones in patients with COVID-19. METHODS: Systematic search was performed on PubMed and Google Scholar until July 18th, 2022. We identified relevant articles that discussed the effects of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on male fertility. RESULTS: A total number of 1,684 articles were identified by using a suitable keyword search strategy. After screening, 26 articles were considered eligible for inclusion in this study. These articles included a total of 1,960 controls and 2,106 patients. When all studies were considered, the results showed that the semen parameters and sex hormone levels of patients infected with SARS-CoV-2 exhibited some significant differences compared with controls. Fortunately, these differences gradually disappear as patients recover from COVID-19. CONCLUSION: While present data show the negative effects of SARS-CoV-2 infection on male fertility, this does not appear to be long-term. Semen quality and hormone levels will gradually increase to normal as patients recover.

A bibliometric analysis for relapsed/refractory Non-Hodgkin lymphoma.

OBJECTIVE: This study aimed to analyze the current research status and trends of publications on relapsed/refractory Non-Hodgkin lymphoma (r/r NHL) using CiteSpace software and to know which centers and authors we should follow in the first place while doing research on r/r NHL. MATERIALS AND METHODS: The publications were retrieved from the Web of Science Core collection database, and CiteSpace (5.5.R5) software was used to analyze the authors, institutions, countries, and keywords. RESULTS: A total of 567 publications from 2009 to 2021 were retrieved, and the most fertile authors, institutions, nationalities and keywords in the field of r/r NHL were identified. Pier Luigi Zinzani team, Kensei Tobinai team, Andre Goy team, and Julie M. Vose team are recognized the main research teams in this field. USA makes the greatest contribution having research funds for r/r NHL. Key cluster areas of research include mantle cell lymphoma, pathway, lymphoma, relapse, pixantrone, Non-Hodgkin lymphoma, romidepsin, relapsed, T-cell lymphoma, and activated T cells. According to the keywords' timeline, the research trends of r/r NHL changed from bone marrow transplantation, radioimmunotherapy, chemotherapy to novel target drugs (like ibritumomab tiuxetan, inhibitor) and criteria EBM. CONCLUSIONS: The bibliometric study provides insights into hotspots and trends in the field of r/r NHL in the past 12 years. It serves us to extract useful information from complex data and provide information for clinicians and researchers.

[Analysis of the diagnostic efficiency of combining multiple laboratory hematological indicators in alpha-fetoprotein-negative hepatocellular carcinoma].

Objective: To establish a diagnostic model for alpha-fetoprotein-negative hepatocellular carcinoma (AFP-NHCC) by combining multiple laboratory hematological indicators and explore its clinical diagnostic efficiency. Methods: A total of 124 inpatients, including 110 males and 14 females, aged 57 (51, 66) years, who were first diagnosed with AFP-NHCC in the PLA General Hospital were included from December 2011 to June 2017. Meanwhile, 331 cases of non-HCC were enrolled as the control group, including 279 males and 52 females, aged 58 (51, 63) years old, with 47 cases of hepatitis B virus (HBV) infection, 40 cases of liver cirrhosis, 64 cases of hepatic hemangioma or cysts, 7 cases of liver nodules, 8 cases of fatty liver, 146 cases of non-liver disease and 19 health controls. Subjects in the AFP-NHCC group and the control group were divided into a training group and a validation group. A total of 196 subjects were involved in the training group, including 103 AFP-NHCC patients and 93 non-HCC patients (19 healthy controls, 25 patients with HBV infection, 22 patients with liver cirrhosis, 23 patients with hepatic hemangioma or cyst, and 4 patients with liver nodules). The differences in laboratory parameters were analyzed, and a diagnostic model of AFP-NHCC under different AFP levels was established. Likewise, 259 subjects, including 113 patients with liver disease, were involved in the validation group to verify the diagnostic efficiency of the model for AFP-NHCC. The receiver operating characteristic (ROC) curve was used to analyze the sensitivity and specificity of different models, and the area under the curve (AUC) was calculated to evaluate the diagnostic performance of different models. Results: In the training group, the indicators of AFP-NHCC diagnostic model included platelet (PLT), prothrombin activity (PTA), serum albumin (ALB), prothrombin time (PT) and carbohydrate antigen 19-9 (CA19-9), and the AUC of the model was 0.848 (95%CI: 0.786-0.911) when AFP≤5 µg/L. Similarly, the indicators of AFP-NHCC diagnostic model included PLT, PTA, ALB, PT and hematocrit (HCT), and the AUC of the model was 0.839 (95%CI: 0.780-0.897) when AFP≤10 µg/L. When AFP≤20 µg/L, the indicators of AFP-NHCC diagnostic model contained PLT, PTA, ALB, PT, HCT and AFP, and the AUC of the model was 0.866 (95%CI: 0.815-0.917). The AUC values of these three models were higher than those of AFP and CA19-9 alone for the diagnosis of AFP-NHCC [0.634 (95%CI: 0.560-0.709), 0.691 (95%CI:0.620-0.761), all P<0.05]. The indicators screened by these three models were combined to establish the final diagnostic model, and the AUC of the model was 0.873 (95%CI: 0.824-0.923), with the sensitivity of 78.6% (81/103) and the specificity of 81.7% (76/93). In the validation group, the predictive AUC of the final model in liver disease patients was 0.892 (95%CI: 0.832-0.951), with the sensitivity of 100% (21/21) and the specificity of 71.7% (66/92), while in the total validation population, the predictive AUC was 0.931 (95%CI: 0.890-0.972), with the sensitivity of 100.0% (21/21) and the specificity of 75.6% (180/238). Conclusion: The final diagnostic model includes PLT, PTA, ALB, PT, HCT, CA19-9 and AFP, which has higher sensitivity and specificity, and has good diagnostic efficiency for the clinical diagnosis of AFP-NHCC.

Circulating exosomal miRNAs and cancer early diagnosis

Microribonucleic acids (miRNAs) are small non-coding ribonucleic acids (ncRNAs), which can affect recognition of homologous sequences and interfere with transcription. It plays key roles in the initiation, development, resistance, metastasis or recurrence of cancers. Identifying circulatory indicators will positively improve the prognosis and quality of life of patients with early cancer. Previous studies have shown that miRNA is highly involved in cancer. In addition, miRNA derived from cancers can be encapsulated as exosomes and further extracted into circulatory systems to realize malignant functions. It indicates that circulating exosome-derived miRNAs have the potential to replace conventional biomarkers as cancer derived exosomes carrying miRNAs can be identified by specific markers and might be more stable and accurate for early diagnosis.

Circulating exosomal miRNAs and cancer early diagnosis.

Microribonucleic acids (miRNAs) are small non-coding ribonucleic acids (ncRNAs), which can affect recognition of homologous sequences and interfere with transcription. It plays key roles in the initiation, development, resistance, metastasis or recurrence of cancers. Identifying circulatory indicators will positively improve the prognosis and quality of life of patients with early cancer. Previous studies have shown that miRNA is highly involved in cancer. In addition, miRNA derived from cancers can be encapsulated as exosomes and further extracted into circulatory systems to realize malignant functions. It indicates that circulating exosome-derived miRNAs have the potential to replace conventional biomarkers as cancer derived exosomes carrying miRNAs can be identified by specific markers and might be more stable and accurate for early diagnosis.