A Bayesian System to Detect and Track Outbreaks of Influenza-Like Illnesses Including Novel Diseases.

BACKGROUND: The early identification of outbreaks of both known and novel influenza-like illnesses is an important public health problem. OBJECTIVE: The design and testing of a tool that detects and tracks outbreaks of both known and novel influenza-like illness, such as the SARS-CoV-19 worldwide pandemic, accurately and early. METHODS: This paper describes the ILI Tracker algorithm that first models the daily occurrence of a set of known influenza-like illnesses in hospital emergency departments in a monitored region using findings extracted from patient care reports using natural language processing. We then show how the algorithm can be extended to detect and track the presence of an unmodeled disease which may represent a novel disease outbreak. RESULTS: We include results based on modeling the diseases influenza, respiratory syncytial virus, human metapneumovirus, and parainfluenza for five emergency departments in Allegheny County Pennsylvania from June 1, 2014 through May 31, 2015. We also include the results of detecting the outbreak of an unmodeled disease, which in retrospect was very likely an outbreak of the enterovirus EV-D68. CONCLUSIONS: The results reported in this paper provide support that ILI Tracker was able to track well the incidence of four modeled influenza-like diseases over a one-year period, relative to laboratory confirmed cases, and it was computationally efficient in doing so. The system was alsoable to detect a likely novel outbreak of the enterovirus D68 early in an outbreak that occurred in Allegheny County in 2014, as well as clinically characterize that outbreak disease accurately.

Self-supervised motor imagery EEG recognition model based on 1-D MTCNN-LSTM network.

Objective.Aiming for the research on the brain-computer interface (BCI), it is crucial to design a MI-EEG recognition model, possessing a high classification accuracy and strong generalization ability, and not relying on a large number of labeled training samples.Approach.In this paper, we propose a self-supervised MI-EEG recognition method based on self-supervised learning with one-dimensional multi-task convolutional neural networks and long short-term memory (1-D MTCNN-LSTM). The model is divided into two stages: signal transform identification stage and pattern recognition stage. In the signal transform recognition phase, the signal transform dataset is recognized by the upstream 1-D MTCNN-LSTM network model. Subsequently, the backbone network from the signal transform identification phase is transferred to the pattern recognition phase. Then, it is fine-tuned using a trace amount of labeled data to finally obtain the motion recognition model.Main results.The upstream stage of this study achieves more than 95% recognition accuracy for EEG signal transforms, up to 100%. For MI-EEG pattern recognition, the model obtained recognition accuracies of 82.04% and 87.14% with F1 scores of 0.7856 and 0.839 on the datasets of BCIC-IV-2b and BCIC-IV-2a.Significance.The improved accuracy proves the superiority of the proposed method. It is prospected to be a method for accurate classification of MI-EEG in the BCI system.

Understanding consumers' continuous-use intention of crowdsourcing logistics services: Empirical evidence from China.

Crowdsourcing logistics-based O2O (online to offline) has been increasingly implemented to help individuals or merchants tackle down the problem of intra city instant delivery in China. However, since insufficient control is imposed on free couriers, consumers are subjected to certain risks generated by the uneven service quality provided by free couriers, such that the continuous-use intention to adopt crowdsourcing logistics may be affected in an unexpected manner. A sampling survey was carried out in China's first- and second-tier cities, with 292 valid questionnaires collected. On that basis, the corresponding hypotheses were tested using the partial least squares (PLS) method. The findings of this study revealed that trust, perceived value, and satisfaction positively contributed to continuous-use intention, where trust contributed the most. Perceived risk exerted a significant negative effect on continuous use intention. Trust is capable of notably reducing perceived risk. Crowdsourcing logistics service quality is the critical driving variable of perceived value and satisfaction. Perceived risk has a negative moderating effect on satisfaction-continuous-use intention relationship, showing that the higher the perceived risk, the weaker the effect of satisfaction on continuous-use intention. Given perceived risk, a conceptual model was built by integrating e-CSI model (e-Customer Satisfaction Index Model) and PAM-ISC model (Post-acceptance Model of IS Continuance Model). From the integration, the findings of this study are expected to provide decision-making basis for crowdsourcing logistics platforms to help solve the "last mile" delivery problem.

Prognostic factors for liver metastasis in patients with small intestinal stromal tumor: A retrospective analysis of surveillance, epidemiology, and end results.

BACKGROUND: Liver metastasis (LIM) is the most common distant site of metastasis in small intestinal stromal tumors (SISTs). The aim of this study was to determine the risk and prognostic factors associated with LIM in patients with SISTs. METHODS: Patients diagnosed with gastrointestinal stromal tumors between 2010 and 2019 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic regression models, as well as a Cox regression model were used to explore the risk factors associated with the development and prognosis of LIM. Additionally, the overall survival (OS) of patients with LIM was analyzed using the Kaplan-Meier method. Furthermore, a predictive nomogram was constructed, and the model's performance was evaluated using receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). RESULTS: A total of 1582 eligible patients with SISTs were included, among whom 146 (9.2%) were diagnosed with LIM. Poor tumor grade, absence of surgery, later T-stage, and no chemotherapy were associated with an increased risk of developing LIM. The nomogram prediction model achieved an AUC of 0.810, 95% Confidence Interval (CI) 0.773-0.846, indicating good performance, and the calibration curve showed excellent accuracy in predicting LIM. The OS rate of patients with LIM was significantly lower than that of patients without LIM (p < 0.001). CONCLUSIONS: Patients with SISTs who are at high risk of developing LIM deserve more attention during follow-up, as LIM can significantly affect patient prognosis. The nomogram demonstrated good calibration and discrimination for predicting LIM.

[Contribution of Kablukov A.F. to development of surgery in the Taurida gubernatorial hospital]. / Vklad A.F. Kablukova v razvitie khirurgii v Tavricheskoi gubernskoi zemskoi bol'nitse.

The authors discuss the contribution of the outstanding Simferopol surgeon Alexander Fedorovich Kablukov to development of surgery in the Taurida gubernia at the end of the 19th - beginning of the 20th century. To date, there is no detailed information about his biography in the literature. The only fact associated with this surgeon is his famous surgery, i.e. the first cholecystectomy described in detail in national literature. Considering pre-revolutionary sources and archival documents, we established that A.F. Kablukov contributed to the new quality level of surgical treatment in the Taurida gubernatorial hospital (the largest hospital in this gubernia).

IiAGL6 participates in the regulation of stamen development and pollen formation in Isatis indigotica.

The AGL6 (AGMOUSE LIKE 6) gene is a member of the SEP subfamily and functions as an E-class floral homeotic gene in the development of floral organs. In this study, we cloned IiAGL6, the orthologous gene of AGL6 in Isatis indigotica. The constitutive expression of IiAGL6 in Arabidopsis thaliana resulted in a late-flowering phenotype and the development of curly leaves during the vegetative growth period. Abnormal changes in floral organ development were observed during the reproductive stage. In woad plants, suppression of IiAGL6 using TRV-VIGS (tobacco rattle virus-mediated virus-induced gene silencing) decreased the number of stamens and led to the formation of aberrant anthers. Similar changes in stamen development were also observed in miRNA-AGL6 transgenic Arabidopsis plants. Yeast two-hybrid and BiFC tests showed that IiAGL6 can interact with other MADS-box proteins in woad; thus, playing a key role in defining the identities of floral organs, particularly during stamen formation. These findings might provide novel insights and help investigate the biological roles of MADS transcription factors in I. indigotica.

Influence analysis of network evolution on Parrondo effect.

Parrondo's paradox is a scheme used to describe an interesting paradoxical situation that a losing Game A and a losing Game B played randomly or periodically will produce a winning result. Here, a dynamic process of network evolution of Link A + Game B is proposed to yield the Parrondo effect. Game B with two asymmetric branches depends on the relative comparison between the capital of the network node and the average capital of all its neighbors. Simulation results demonstrate that network structure evolution make the losing Game B produce a paradoxical effect of winning and would be advantageous to the development of the "ratcheting" mechanism in Game B. Furthermore, the underlying paradoxical mechanism is analyzed to illustrate the parameter space where the "strong" Parrondo effect occurs. Then influence of two types of network connection is analyzed, indicating that the "agitating" effect of the network is basically the same when a node connects to a neighbor's neighbor or randomly chooses a node other than its neighbors. Further, a higher frequency of network evolution yields a larger parameter region where the "strong" Parrondo effect emerges.

Response of a submerged macrophyte (Vallisneria natans) to water depth gradients and sediment nutrient concentrations.

Submerged plants constitute a vital component of shallow lake ecosystems, where water depth and sediment nitrogen­phosphorus content are two key factors influencing their growth. This study focuses on Vallisneria natans and investigates the morphological and physiological changes of V. natans under the interaction of three water depth gradients and two different sediment nutrient levels. It explores the mechanisms through which varying sediment nutrient conditions under different water depths affect the growth of V. natans. The results indicate that both independent and interactive effects of water depth and sediment nutrient status significantly impact the morphology, antioxidant enzyme activity, and photosynthetic pigment content of V. natans, with water depth having a greater influence. To adapt to increased water depth-induced light stress, V. natans responds morphologically by increasing leaf length, leaf width, and decreasing maximum root length. Physiologically, it enhances its antioxidant regulation capacity and photosynthetic efficiency by increasing antioxidant enzyme activity, root vitality, and photosynthetic pigment content to counter weak light stress. However, these adaptations are insufficient to cope with excessively deep waters (200 cm). Sediment nutrient levels primarily control the growth of V. natans by affecting its root system. When sediment nitrogen and phosphorus content is lower, V. natans exhibits greater total root volume and surface area to enhance nutrient absorption efficiency. Water depth not only directly influences the growth of submerged plants but may also impact the migration and transformation of phosphorus in sediments, further exacerbating its effects on the growth of these plants, thus accelerating the regime shift of shallow lakes. Therefore, this study reveals V. natans' response strategies to varying water depths and sediment nutrient levels, determining suitable water levels and sediment nutrient conditions for its growth. These research findings provide a scientific basis for water level management and ecological restoration of submerged aquatic plants in shallow lakes.

[A multicenter randomized controlled trial of domestic robot-assisted and conventional total knee arthroplasty].

Objective: To investigate the accuracy, safety, and short-term effectiveness of a domestic robot-assisted system in total knee arthroplasty (TKA) by a multicenter randomized controlled trial. Methods: Between December 2021 and February 2023, 138 patients with knee osteoarthritis who received TKA in 5 clinical centers were prospectively collected, and 134 patients met the inclusion criteria were randomly assigned to either a trial group ( n=68) or a control group ( n=66). Seven patients had lost follow-up and missing data, so they were excluded and the remaining 127 patients were included for analysis, including 66 patients in the trial group and 61 patients in the control group. There was no significant difference ( P>0.05) in gender, age, body mass index, side, duration of osteoarthritis, Kellgren-Lawrence grading, preoperative Knee Society Score (KSS) and Western Ontario and McMaster University Osteoarthritis Index (WOMAC) score between the two groups. The trial group completed the TKA by domestic robot-assisted osteotomy according to the preoperative CT-based surgical planning. The control group was performed by traditional osteotomy plate combined with soft tissue release. Total operation time, osteotomy time of femoral/tibial side, intraoperative blood loss, and postoperative complications were recorded and compared between the two groups. The radiographs were taken at 5 and 90 days after operation, and hip-knee-ankle angle (HKA), lateral distal angle of femur (LDFA), and posterior tibial slope (PTS) were measured. The difference between the measured values of the above indexes at two time points after operation and the preoperative planning target values was calculated, and the absolute value (absolute error) was taken for comparison between the two groups. The postoperative recovery of lower limb alignment was judged and the accuracy was calculated. KSS score and WOMAC score were used to evaluate the knee joint function of patients before operation and at 90 days after operation. The improvement rates of KSS score and WOMAC score were calculated. The function, stability, and convenience of the robot-assisted system were evaluated by the surgeons. Results: The total operation time and femoral osteotomy time of the trial group were significantly longer than those of the control group ( P<0.05). There was no significant difference in the tibial osteotomy time and the amount of intraoperative blood loss between the two groups ( P>0.05). The incisions of both groups healed by first intention after operation, and there was no infection around the prosthesis. Nine patients in the trial group and 8 in the control group developed lower extremity vascular thrombosis, all of which were calf intermuscular venous thrombosis, and there was no significant difference in the incidence of complications ( P>0.05). All patients were followed up 90 days. There was no significant difference in KSS score and WOMAC score between the two groups at 90 days after operation ( P>0.05). There was significant difference in the improvement rate of KSS score between the two groups ( P<0.05), while there was no significant difference in the improvement rate of WOMAC score between the two groups ( P>0.05). Radiological results showed that the absolute errors of HKA and LDFA in the trial group were significantly smaller than those in the control group at 5 and 90 days after operation ( P<0.05), and the recovery accuracy of lower limb alignment was significantly higher than that in control group ( P<0.05). The absolute error of PTS in the trial group was significantly smaller than that in the control group at 5 days after operation ( P<0.05), but there was no significant difference at 90 days between the two groups ( P>0.05). The functional satisfaction rate of the robot-assisted system was 98.5% (65/66), and the satisfaction rates of stability and convenience were 100% (66/66). Conclusion: Domestic robot-assisted TKA is a safe and effective surgical treatment for knee osteoarthritis, which can achieve favorable lower limb alignment reconstruction, precise implant of prosthesis, and satisfactory functional recovery.

Survival analysis of extragastrointestinal stromal tumors based on the SEER database: a population-based study.

BACKGROUND: Extragastrointestinal stromal tumors (EGISTs) are rare mesenchymal neoplasms that originate outside the gastrointestinal tract. However, the population-level survival analysis of EGIST remains poorly grasped. Therefore, we aimed to analyze the survival of EGIST patients using the Surveillance, Epidemiology, and End Results (SEER) database. METHODS: All patients diagnosed with GIST and EGIST between 2000 and 2019 were identified through utilization of the SEER database. Missing data were handled using multiple imputation methodology. Kaplan-Meier analyses and Cox proportional hazard models were employed to assess the influence of demographic and clinical characteristics on both overall survival (OS) and cancer-specific survival (CSS). RESULTS: A total of 13,330 patients were enrolled in the study, comprising 12,627 diagnosed with GIST and 703 with EGIST. EGIST patients demonstrated significantly poorer OS [hazard ratio (HR) 1.732, 95% confidence interval (CI) 1.522-1.970, P < 0.001] and CSS (HR 2.167, 95% CI 1.821-2.577, P < 0.001) compared to GIST patients. The mean 1-year, 3-year, 5-year, and 10-year OS rates for EGIST patients were 78.3%, 61.9%, 50.5%, and 32.5%, respectively, with corresponding mean CSS rates of 84.3%, 70.8%, 61.3%, and 46.5%. Multivariate Cox regression analysis identified age, race, sex, grade, size, and surgical type as independent risk factors for OS in EGIST patients, while age, sex, year of diagnosis, grade, surgical type, and radiation therapy were identified as independent risk factors for CSS. Patients with EGIST who underwent surgical treatment exhibited significantly higher 5-year OS rates (49.0% vs. 39.9%, P = 0.035) and CSS rates (63.9% vs. 53.0%, P = 0.028) compared to those who did not undergo surgery. CONCLUSIONS: EGIST patients have a poorer prognosis compared to GIST patients; however, surgical treatment has been shown to improve the prognosis.

Synergistic treatment of carbon dioxide and nitrogen-containing wastewater by electrochemical C-N coupling.

Electrocatalytic CO2 reduction technology has been considered a promising approach to alleviate the severe environmental and energy issues caused by the anthropogenic over-emission of CO2. Coupling CO2 reduction with nitrogen (N)-pollutants reduction from wastewater to produce higher valued products (e.g., urea, amide, amine, etc.) could significantly extend the application scenarios and product categories of CO2 reduction technologies. This paper investigates the available CO2 and N-pollutants sources and summarizes the recent progress of electrocatalytic C-N coupling reactions. Based on the fundamental research, technical concerns for scale-up applications of C-N coupling electrocatalysis are thoroughly discussed. Finally, we prospect the opportunities and challenges with an in-depth understanding of the underlying dominant factors in applying C-N coupling electrocatalysis. Further development in recycling CO2 and N pollutants via the electrocatalytic C-N coupling process is also discussed.

A Bayesian System to Track Outbreaks of Influenza-Like Illnesses Including Novel Diseases.

It would be highly desirable to have a tool that detects the outbreak of a new influenza-like illness, such as COVID-19, accurately and early. This paper describes the ILI Tracker algorithm that first models the daily occurrence of a set of known influenza-like illnesses in a hospital emergency department using findings extracted from patient-care reports using natural language processing. We include results based on modeling the diseases influenza, respiratory syncytial virus, human metapneumovirus, and parainfluenza for five emergency departments in Allegheny County Pennsylvania from June 1, 2010 through May 31, 2015. We then show how the algorithm can be extended to detect the presence of an unmodeled disease which may represent a novel disease outbreak. We also include results for detecting an outbreak of an unmodeled disease during the mentioned time period, which in retrospect was very likely an outbreak of Enterovirus D68.

Risk factors for postoperative complications in endoscopic resection of gastric gastrointestinal stromal tumors: a multi-center analysis.

BACKGROUND: Endoscopic resection (ER) is widely used in treating gastric gastrointestinal stromal tumors (gGISTs); however, complications occur frequently after resection. We aimed to determine factors associated with postoperative complications for ER of gGISTs. METHODS: This was a retrospective, multi-center, observational study. Consecutive patients who underwent ER of gGISTs at five institutes from January 2013 to December 2022 were analyzed. The risk factors for delayed bleeding and postoperative infection were assessed. RESULTS: A total of 513 cases were finally analyzed. Of 513 patients, 27 (5.3%) had delayed bleeding and 69 (13.4%) had a postoperative infection. Multivariate analysis indicated that risk factors for delayed bleeding were long operative time (OR = 50.655; 95% CI, 13.777-186.252; P < 0.001) and severe intraoperative bleeding (OR = 4.731, 95% CI, 1.139-19.658; P = 0.032), and risk factors for postoperative infection were long operative time (OR = 13.749, 95% CI, 6.884-27.461; P < 0.001) and perforation (OR = 4.339, 95% CI, 2.178-8.644; P < 0.001). CONCLUSIONS: Our study indicated the risk factors for postoperative complications in ER of gGISTs. Long operation time is a common risk factor for delayed bleeding and postoperative infection. Patients with these risk factors should be given careful observation postoperatively.

Identification of predictors of long-term survival and prognostic outcomes in thymic squamous cell carcinoma: A real-world study.

BACKGROUND: Although thymic squamous cell carcinoma (TSCC) is among the most prevalent forms of thymic carcinoma, there are relatively few studies on this tumor type, and its staging, optimal treatment strategies, and relevant prognostic factors remain controversial. METHODS: The present study analyzed 79 patients diagnosed with TSCC between January 2008 and January 2021. Kaplan-Meier curves and Cox univariate and multivariate regression analyses were used to explore factors associated with overall survival (OS) and progression-free survival (PFS) in the overall patient cohort and patient subgroups stratified according to the TNM stage. Time-dependent receiver operating characteristic (ROC) analyses were used to compare the TNM and Masaoka systems as predictors of patient prognosis. RESULTS: The 5- and 10-year OS rates in this study were 65.5% and 49.4%, respectively, with corresponding 5- and 10-year PFS rates of 52.3% and 37.9%. Survival outcomes were better for patients with early-stage disease (p < 0.001) and patients that underwent surgical treatment (p < 0.001). Neither extent of resection (p = 0.820) nor the surgical approach (p = 0.444) influenced patient survival. In individuals with advanced disease, all forms of adjuvant therapy including radiotherapy (p = 0.021), chemotherapy (p = 0.035), and chemoradiation (p = 0.01) significantly improved patient PFS, but only adjuvant chemoradiotherapy improved patient OS (p = 0.035). When predicting the patient survival outcomes, the TNM system was slightly superior to the Masaoka system (area under the ROC curve [AUC] at 5 years: OS, 0.742 vs. 0.723; PFS, 0.846 vs. 0.816). CONCLUSION: TSCC is an orphan malignancy with a poor prognosis. TNM staging may be superior to Masaoka staging as a predictor of TSCC patient prognosis. Surgery is the mainstay of TSCC treatment. Video-assisted thoracoscopy (VATS) should be considered for selected patients. Multimodal therapy was associated with excellent results for patients with advanced TNM stage, particularly when surgery was accompanied by adjuvant chemoradiation.

HSF1 inhibits microglia activation to attenuate neuroinflammation via regulating miR-214-3p and NFATc2 in Parkinson's disease.

Parkinson's disease (PD) is characterized by microglia activation that leads to neuroinflammation. Heat shock transcription factor 1 (HSF1) is known to exert neuroprotective effects on neurodegenerative diseases. This study sought to analyse the role and mechanism of HSF1 in PD-induced neuroinflammation. The PD mouse models were established using 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). Animal behaviour capacities and neuronal damage were assessed via behavioural tests, tyrosine hydroxylase (TH) staining, and immunofluorescence. Levels of HSF1, miR-214-3p, nuclear factor of activated T cells 2 (NFATc2), and neuroinflammatory factors were detected via RT-qPCR, Western blotting, and ELISA.Binding relationships between HSF1 and miR-214-3p, miR-214-3p, and NFATc2 were tested via dual-luciferase or chromatin immunoprecipitation assays. Functional rescue experiments were designed to confirm the roles of miR-214-3p and NFATc2. HSF1 expression in brain tissues was downregulated upon MPTP treatment. HSF1 overexpression reduced motor deficits and loss of dopaminergic neurons, increased TH-positive neurons, and repressed neuroinflammation and micro-glia activation. Mechanically, HSF1 bound to the miR-214-3p promoter to increase its expression and inhibited NFATc2 transcription. miR-214-3p downregulation or NFATc2 overexpression reversed the inhibition of HSF1 overexpression on neuroinflammation and microglia activation. Overall, our findings unveiled the therapeutic role of HSF1 in PD-induced neuroinflammation and microglia activation via regulating miR-214-3p and NFATc2.

Risk factors for conversion from endoscopic resection to laparoscopic resection for gastric gastrointestinal stromal tumors.

OBJECTIVE: Endoscopic resection (ER) of gastric gastrointestinal stromal tumors (gGISTs) is a commonly used treatment; however, it is associated with a risk of conversion to laparoscopic resection (LR). This study was performed to identify factors influencing conversion from ER to LR and the effects of conversion on outcomes. METHODS: The clinicopathological features of patients treated for gGISTs from March 2010 to May 2021 were retrospectively collected. Endpoints included the determination of risk factors associated with LR conversion, with comparisons of surgical outcomes with and without conversion. Propensity score matching was performed to compare the two groups. RESULTS: In total, 371 gGISTs were analyzed. Sixteen patients required conversion from ER to LR. Propensity score matching demonstrated that invasion depth (muscularis propria with exophytic growth) and gGIST size (≥3 cm) were independent risk factors for conversion to LR. The procedure duration (median, 160.5 vs. 60.0 minutes), postoperative hospitalization duration (median, 8 vs. 6 days), and postoperative fasting duration (median, 5 vs. 3 days) were significantly longer in patients who underwent conversion to LR. CONCLUSIONS: Accurate preoperative measurements of tumor size and invasion depth may help determine more appropriate surgical approaches for patients with gGISTs.

Industry, occupation, and exposure history of mesothelioma patients in the U.S. National Mesothelioma Virtual Bank, 2006-2022.

BACKGROUND: Malignant mesothelioma is associated with environmental and occupational exposure to certain mineral fibers, especially asbestos. This study aims to examine work histories of mesothelioma patients and their survival time. METHOD: Using the NIOSH Industry and Occupation Computerized Coding System, we mapped occupations and industries recorded for 748 of 1444 patients in the U.S. National Mesothelioma Virtual Bank (NMVB) during the period 2006-2022. Descriptive and survival analyses were conducted. RESULTS: Among the 1023 industries recorded for those having mesothelioma, the most frequent cases were found for those in manufacturing (n = 225, 22.0%), construction (138, 13.5%), and education services (66, 6.5%); among the 924 occupation records, the most frequent cases were found for those in construction and extraction (174, 18.8%), production (145, 15.7%), and management (84, 9.1%). Males (583) or persons aged >40 years (658) at the time of diagnosis tended to have worked in industries traditionally associated with mesothelioma (e.g., construction), while females (163) or persons aged 20-40 years (27) tended to have worked in industries not traditionally associated with mesothelioma (e.g., health care). Asbestos, unknown substances, and chemical solvents were the most frequently reported exposure, with females most often reporting an unknown substance. A multi-variable Cox Hazard Regression analysis showed that significant prognostic factors associated with decreased survival in mesothelioma cases are sex (male) and work experience in utility-related industry, while factor associated with increased survival are epithelial or epithelioid histological type, prior history of surgery and immunotherapy, and industry experience in accommodation and food services. CONCLUSION: The NMVB has the potential of serving as a sentinel surveillance mechanism for identifying industries and occupations not traditionally associated with mesothelioma. Results indicate the importance of considering all potential sources of asbestos exposures including occupational, environmental, and extra-occupational exposures when evaluating mesothelioma patients and advising family members.

A multicenter randomized controlled trial of domestic robot-assisted and conventional total knee arthroplasty / 中国修复重建外科杂志

OBJECTIVE@#To investigate the accuracy, safety, and short-term effectiveness of a domestic robot-assisted system in total knee arthroplasty (TKA) by a multicenter randomized controlled trial.@*METHODS@#Between December 2021 and February 2023, 138 patients with knee osteoarthritis who received TKA in 5 clinical centers were prospectively collected, and 134 patients met the inclusion criteria were randomly assigned to either a trial group ( n=68) or a control group ( n=66). Seven patients had lost follow-up and missing data, so they were excluded and the remaining 127 patients were included for analysis, including 66 patients in the trial group and 61 patients in the control group. There was no significant difference ( P>0.05) in gender, age, body mass index, side, duration of osteoarthritis, Kellgren-Lawrence grading, preoperative Knee Society Score (KSS) and Western Ontario and McMaster University Osteoarthritis Index (WOMAC) score between the two groups. The trial group completed the TKA by domestic robot-assisted osteotomy according to the preoperative CT-based surgical planning. The control group was performed by traditional osteotomy plate combined with soft tissue release. Total operation time, osteotomy time of femoral/tibial side, intraoperative blood loss, and postoperative complications were recorded and compared between the two groups. The radiographs were taken at 5 and 90 days after operation, and hip-knee-ankle angle (HKA), lateral distal angle of femur (LDFA), and posterior tibial slope (PTS) were measured. The difference between the measured values of the above indexes at two time points after operation and the preoperative planning target values was calculated, and the absolute value (absolute error) was taken for comparison between the two groups. The postoperative recovery of lower limb alignment was judged and the accuracy was calculated. KSS score and WOMAC score were used to evaluate the knee joint function of patients before operation and at 90 days after operation. The improvement rates of KSS score and WOMAC score were calculated. The function, stability, and convenience of the robot-assisted system were evaluated by the surgeons.@*RESULTS@#The total operation time and femoral osteotomy time of the trial group were significantly longer than those of the control group ( P<0.05). There was no significant difference in the tibial osteotomy time and the amount of intraoperative blood loss between the two groups ( P>0.05). The incisions of both groups healed by first intention after operation, and there was no infection around the prosthesis. Nine patients in the trial group and 8 in the control group developed lower extremity vascular thrombosis, all of which were calf intermuscular venous thrombosis, and there was no significant difference in the incidence of complications ( P>0.05). All patients were followed up 90 days. There was no significant difference in KSS score and WOMAC score between the two groups at 90 days after operation ( P>0.05). There was significant difference in the improvement rate of KSS score between the two groups ( P<0.05), while there was no significant difference in the improvement rate of WOMAC score between the two groups ( P>0.05). Radiological results showed that the absolute errors of HKA and LDFA in the trial group were significantly smaller than those in the control group at 5 and 90 days after operation ( P<0.05), and the recovery accuracy of lower limb alignment was significantly higher than that in control group ( P<0.05). The absolute error of PTS in the trial group was significantly smaller than that in the control group at 5 days after operation ( P<0.05), but there was no significant difference at 90 days between the two groups ( P>0.05). The functional satisfaction rate of the robot-assisted system was 98.5% (65/66), and the satisfaction rates of stability and convenience were 100% (66/66).@*CONCLUSION@#Domestic robot-assisted TKA is a safe and effective surgical treatment for knee osteoarthritis, which can achieve favorable lower limb alignment reconstruction, precise implant of prosthesis, and satisfactory functional recovery.

Prediction of COVID-19 Patients' Emergency Room Revisit using Multi-Source Transfer Learning.

The coronavirus disease 2019 (COVID-19) has led to a global pandemic of significant severity. In addition to its high level of contagiousness, COVID-19 can have a heterogeneous clinical course, ranging from asymptomatic carriers to severe and potentially life-threatening health complications. Many patients have to revisit the emergency room (ER) within a short time after discharge, which significantly increases the workload for medical staff. Early identification of such patients is crucial for helping physicians focus on treating life-threatening cases. In this study, we obtained Electronic Health Records (EHRs) of 3,210 encounters from 13 affiliated ERs within the University of Pittsburgh Medical Center between March 2020 and January 2021. We leveraged a Natural Language Processing technique, ScispaCy, to extract clinical concepts and used the 1001 most frequent concepts to develop 7-day revisit models for COVID-19 patients in ERs. The research data we collected were obtained from 13 ERs, which may have distributional differences that could affect the model development. To address this issue, we employed a classic deep transfer learning method called the Domain Adversarial Neural Network (DANN) and evaluated different modeling strategies, including the Multi-DANN algorithm (which considers the source differences), the Single-DANN algorithm (which doesn't consider the source differences), and three baseline methods: using only source data, using only target data, and using a mixture of source and target data. Results showed that the Multi-DANN models outperformed the Single-DANN models and baseline models in predicting revisits of COVID-19 patients to the ER within 7 days after discharge (median AUROC = 0.8 vs. 0.5). Notably, the Multi-DANN strategy effectively addressed the heterogeneity among multiple source domains and improved the adaptation of source data to the target domain. Moreover, the high performance of Multi-DANN models indicates that EHRs are informative for developing a prediction model to identify COVID-19 patients who are very likely to revisit an ER within 7 days after discharge.

Musical Emotions Recognition Using Entropy Features and Channel Optimization Based on EEG.

The dynamic of music is an important factor to arouse emotional experience, but current research mainly uses short-term artificial stimulus materials, which cannot effectively awaken complex emotions and reflect their dynamic brain response. In this paper, we used three long-term stimulus materials with many dynamic emotions inside: the "Waltz No. 2" containing pleasure and excitement, the "No. 14 Couplets" containing excitement, briskness, and nervousness, and the first movement of "Symphony No. 5 in C minor" containing passion, relaxation, cheerfulness, and nervousness. Approximate entropy (ApEn) and sample entropy (SampEn) were applied to extract the non-linear features of electroencephalogram (EEG) signals under long-term dynamic stimulation, and the K-Nearest Neighbor (KNN) method was used to recognize emotions. Further, a supervised feature vector dimensionality reduction method was proposed. Firstly, the optimal channel set for each subject was obtained by using a particle swarm optimization (PSO) algorithm, and then the number of times to select each channel in the optimal channel set of all subjects was counted. If the number was greater than or equal to the threshold, it was a common channel suitable for all subjects. The recognition results based on the optimal channel set demonstrated that each accuracy of two categories of emotions based on "Waltz No. 2" and three categories of emotions based on "No. 14 Couplets" was generally above 80%, respectively, and the recognition accuracy of four categories based on the first movement of "Symphony No. 5 in C minor" was about 70%. The recognition accuracy based on the common channel set was about 10% lower than that based on the optimal channel set, but not much different from that based on the whole channel set. This result suggested that the common channel could basically reflect the universal features of the whole subjects while realizing feature dimension reduction. The common channels were mainly distributed in the frontal lobe, central region, parietal lobe, occipital lobe, and temporal lobe. The channel number distributed in the frontal lobe was greater than the ones in other regions, indicating that the frontal lobe was the main emotional response region. Brain region topographic map based on the common channel set showed that there were differences in entropy intensity between different brain regions of the same emotion and the same brain region of different emotions. The number of times to select each channel in the optimal channel set of all 30 subjects showed that the principal component channels representing five brain regions were Fp1/F3 in the frontal lobe, CP5 in the central region, Pz in the parietal lobe, O2 in the occipital lobe, and T8 in the temporal lobe, respectively.