Evaluating the performance of a binary diagnostic test, including artificial intelligence classification algorithms, involves measuring sensitivity, specificity, positive predictive value, and negative predictive value. Particularly when comparing the performance of two diagnostic tests applied on the same set of patients, these metrics are crucial for identifying the more accurate test. However, comparing predictive values presents statistical challenges because their denominators depend on the test outcomes, unlike the comparison of sensitivities and specificities. This paper reviews existing methods for comparing predictive values and proposes using the permutation test. The permutation test is an intuitive, non-parametric method suitable for datasets with small sample sizes. We demonstrate each method using a dataset from MRI and combined modality of mammography and ultrasound in diagnosing breast cancer.

Porcine cytomegalovirus (PCMV) infection is widespread worldwide and has a high prevalence in swine herds, especially in countries with intensive swine production. PCMV is zoonotic and can impact xenotransplants. It is the third swine virus known to be zoonotic, following swine influenza virus (influenza A) and hepatitis E virus genotype 3 (HEVgt3 or HEV-3). Wild boars, serving as reservoirs for various pathogens, including PCMV, pose a risk to both the pig industry and public health. This study aimed to investigate PCMV infection in Serbian wild boars using real-time PCR and assess other viral infections. We also tested samples for the presence of other viral infections: Aujeszky disease virus (ADV), Porcine parvovirus (PPV) and Porcine reproductive respiratory syndrome (PRRSV). Samples from 50 wild boars across 3 districts were tested. Results showed 8% positivity for PCMV DNA, with females showing higher infection rates. Porcine parvovirus (PPV) was detected in 56% of samples, while Porcine reproductive respiratory syndrome virus (PRRSV) was absent. ADV was found in 18% of samples, primarily in younger animals. This research contributes to understanding PCMV prevalence in Serbian wild boars and emphasizes the importance of monitoring viral infections in wild populations, considering the potential zoonotic and economic implications.

BACKGROUND: Proper diagnosis of tuberculosis (TB) lymphadenitis is critical for its treatment and prevention. Fine needle aspirate cytology (FNAC) is the mainstay method for the diagnosis of TB lymphadenitis in Ethiopia; however, the performance of FNAC has not been evaluated in the Eastern Region of Ethiopia. This study aimed to evaluate the performance of FNAC and Ziehl-Neelsen (ZN) staining compared with that of GeneXpert for the diagnosis of TB lymphadenitis.
METHODS: Fine needle aspiration (FNA) specimens collected from 291 patients suspected of having TB lymphadenitis were examined using FNAC, ZN, and GeneXpert to diagnose TB lymphadenitis. Gene-Xpert was considered the reference standard method for comparison. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and kappa coefficient were determined using SPSS version 25.
RESULTS: The sensitivity, specificity, PPV, and NPV of ZN for diagnosing TB lymphadenitis were 73.2%, 97.4%, 96.2%, and 80.1% respectively. There was poor agreement between ZN and GeneXpert (Kappa=-0.253). The sensitivity, specificity, PPV, and NPV of FNAC were 83.3%, 94.8%, 93.5%, and 86.3% respectively. There was moderate agreement between the FNAC and GeneXpert (Kappa = 0.785).
CONCLUSIONS: The fine needle aspiration cytology (FNAC) is a more sensitive test for the diagnosis of TB lymphadenitis than ZN. The FNAC showed a moderate agreement with the GeneXpert assay. This study recommends the FNA GeneXpert MTB/RIF test in preference to FNAC for the diagnosis of TB lymphadenitis to avoid a missed diagnosis of smear-negative TB lymphadenitis.

In order to study the integration of reticuloendotheliosis virus (REV) in pigeonpox virus (PPV), we collected suspected pigeonpox disease material, amplified the 4b core protein gene of PPV, the gp90 gene of REV, and the integrated sequence fragments from the end of the ORF201 segment of PPV to the beginning of the LTR of REV, and sequenced these genes. The results showed that a 4b core protein fragment of 332 bp was amplified and identified as pigeonpox virus, which was named SX/TY/LTR 01/2023. Sequence analysis showed that the pigeonpox virus isolate belonged to genotype A2, which was the closest to the domestic CVL strain, with a identity of 99.4%. A band of 1191 bp was amplified from the gp90 gene of REV, named SX/TY/PPV-REV01/2023, and sequence analysis indicated that REV belonged to genotype III. The sequence analysis showed that REV belonged to genotype III, and belonged to the same large branch as the domestic isolates JSRD0701 and LNR0801, with 99.3% identity. The integrated sequence fragment was amplified to a band of 637 bp, which determined that the REV sequence was integrated in the PPV rather than a mixed infection of the two viruses. This indicates that REV was integrated in this isolation of PPV, suggesting that pigeon farms need to prevent reticuloendotheliosis at the same time when preventing pigeonpox.

The drill-blasting method is a commonly used mining technique in open-pit mines, and the peak particle velocity (PPV) caused by blasting vibrations is an important indicator for evaluating the rationality of blasting mining design parameters. To develop an effective PPV prediction model, a parameter self-optimizing RUN-XGBoost prediction model is implemented using the Runge-Kutta optimization algorithm (RUN) combined with extreme gradient boosting (XGBoost). The factors affecting the prediction of PPV, including maximum explosive (ME), total explosive (TE), blast center distance (BCD), blast hole depth (BHD), and height difference between the measurement location and the blast location (DH), are selected as the influencing indicators. 188 pieces of blasting operation data were measured at the RK open pit copper-cobalt mine. Then, the RUN-XGBoost prediction model for PPV is studied and compared with the Sadovsky empirical formula, traditional XGBoost model, PSO-XGBoost model, and some traditional machine learning models (Ridge, LASSO, SVM, and SVR) using R2, RMSE, VAF, MAE, and MBE as evaluation indicators for model prediction results. Finally, the Shapley Additive Explanations (SHAP) method is used to evaluate the contribution of different influencing indicators to the PPV prediction results. The results show that the RUN-XGBoost prediction model is significantly better than other machine learning models and the Sadovsky empirical formula in the prediction of PPV, further demonstrating that the RUN-XGBoost prediction model can handle the nonlinear features of multiple factors and provide a reliable, simple, and effective PPV prediction model, forming a rapid prediction and evaluation method for blasting vibrations in open-pit mining.

Southern Africa Territories 2 (SAT2) foot-and-mouth disease (FMD) has crossed long-standing regional boundaries in recent years and entered the Middle East. However, the existing vaccines offer poor cross-protection against the circulating strains in the field. Therefore, there is an urgent need for an alternative design approach for vaccines in anticipation of a pandemic of SAT2 Foot-and-mouth disease virus (FMDV). The porcine parvovirus (PPV) VP2 protein can embed exogenous epitopes into the four loops on its surface, assemble into virus-like particles (VLPs), and induce antibodies and cytokines to PPV and the exogenous epitope. In this study, chimeric porcine parvovirus VP2 VLPs (chimeric PPV-SAT2-VLPs) expressing the T-and/or B-cell epitopes of the structural protein VP1 of FMDV SAT2 were produced using the recombinant pFastBac™ Dual vector of baculoviruses in Sf9 and HF cells We used the Bac-to-Bac system to construct the recombinant baculoviruses. The VP2-VLP--SAT2 chimeras displayed chimeric T-cell epitope (amino acids 21-40 of VP1) and/or the B-cell epitope (amino acids 135-174) of SAT FMDV VP1 by substitution of the corresponding regions at the N terminus (amino acids 2-23) and/or loop 2 and/or loop 4 of the PPV VP2 protein, respectively. In mice, the chimeric PPV-SAT2-VLPs induced specific antibodies against PPV and the VP1 protein of SAT2 FMDV. The VP2-VLP-SAT2 chimeras induced specific antibodies to PPV and the VP1 protein specific epitopes of FMDV SAT2. In this study, as a proof-of-concept, successfully generated chimeric PPV-VP2 VLPs expressing epitopes of the structural protein VP1 of FMDV SAT2 that has a potential to prevent FMDV SAT2 and PPV infection in pigs.

BACKGROUND: Obesity increases the risk of morbidity and mortality during surgical procedures. Goal-directed fluid therapy (GDFT) is a new concept for perioperative fluid management that has been shown to improve patient prognosis. This study aimed to investigate the role of the Pleth Variability Index (PVI), systolic pressure variation (SPV), and pulse pressure variation (PPV) in maintaining tissue perfusion and renal function during GDFT management in patients undergoing laparoscopic sleeve gastrectomy (LSG).
METHODS: Two hundred ten patients were enrolled in our prospective randomized controlled clinical trial. Demographic data, hemodynamic parameters, biochemical parameters, the amount of crystalloid and colloid fluid administered intraoperatively, and the technique of goal-directed fluid management used were recorded. Patients were randomly divided into three groups: PVI (n = 70), PPV (n = 70), and SPV (n = 70), according to the technique of goal-directed fluid management. Postoperative nausea and vomiting, time of return of bowel movement, and hospital stay duration were recorded.
RESULTS: There was no statistically significant difference between the number of crystalloids administered in all three groups. However, the amount of colloid administered was statistically significantly lower in the SPV group than in the PVI group, and there was no significant difference in the other groups. Statistically, there was no significant difference between the groups in plasma lactate, blood urea, and creatinine levels.
CONCLUSIONS: In LSG, dynamic measurement techniques such as PVI, SPV, and PPV can be used in patients with morbid obesity without causing intraoperative and postoperative complications. PVI may be preferred over other invasive methods because it is noninvasive.

OBJECTIVE: The use of goal-directed fluid therapy (GDFT) has been shown to reduce complications and improve prognosis in high-risk abdominal surgery patients. However, the utilization of pulse pressure variation (PPV) guided GDFT in laparoscopic surgery remains a subject of debate. We hypothesized that utilizing PPV guidance for GDFT would optimize short-term prognosis in elderly patients undergoing laparoscopic radical resection for colorectal cancer compared to conventional fluid therapy.
METHODS: Elderly patients undergoing laparoscopic radical resection of colorectal cancer were randomized to receive either PPV guided GDFT or conventional fluid therapy and explore whether PPV guided GDFT can optimize the short-term prognosis of elderly patients undergoing laparoscopic radical resection of colorectal cancer compared with conventional fluid therapy.
RESULTS: The incidence of complications was significantly lower in the PPV group compared to the control group (32.8% vs. 57.1%, P = .009). Additionally, the PPV group had a lower occurrence of gastrointestinal dysfunction (19.0% vs. 39.3%, P = .017) and postoperative pneumonia (8.6% vs. 23.2%, P = .033) than the control group.
CONCLUSIONS: Utilizing PPV as a monitoring index for GDFT can improve short-term prognosis in elderly patients undergoing laparoscopic radical resection of colorectal cancer.
BACKGROUND: ChiCTR2300067361; date of registration: January 5, 2023.

UNASSIGNED: This study aimed to develop a deep learning system to identify and differentiate the metastatic cervical lymph nodes (CLNs) of thyroid cancer.
UNASSIGNED: From January 2014 to December 2020, 3059 consecutive patients with suspected with metastatic CLNs of thyroid cancer were retrospectively enrolled in this study. All CLNs were confirmed by fine needle aspiration. The patients were randomly divided into the training (1228 benign and 1284 metastatic CLNs) and test (307 benign and 240 metastatic CLNs) groups. Grayscale ultrasonic images were used to develop and test the performance of the Y-Net deep learning model. We used the Y-Net network model to segment and differentiate the lymph nodes. The Dice coefficient was used to evaluate the segmentation efficiency. Sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were used to evaluate the classification efficiency.
UNASSIGNED: In the test set, the median Dice coefficient was 0.832. The sensitivity, specificity, accuracy, PPV, and NPV were 57.25%, 87.08%, 72.03%, 81.87%, and 66.67%, respectively. We also used the Y-Net classified branch to evaluate the classification efficiency of the LNs ultrasonic images. The classification branch model had sensitivity, specificity, accuracy, PPV, and NPV of 84.78%, 80.23%, 82.45%, 79.35%, and 85.61%, respectively. For the original ultrasonic reports, the sensitivity, specificity, accuracy, PPV, and NPV were 95.14%, 34.3%, 64.66%, 59.02%, 87.71%, respectively. The Y-Net model yielded better accuracy than the original ultrasonic reports.
UNASSIGNED: The Y-Net model can be useful in assisting sonographers to improve the accuracy of the classification of ultrasound images of metastatic CLNs.

UNASSIGNED: The elastic scattering spectroscopy (ESS) device (DermaSensor Inc., Miami, FL) is a noninvasive, painless, adjunctive tool for skin cancer detection.
UNASSIGNED: To investigate the performance of the ESS device in the detection of melanoma.
UNASSIGNED: A prospective, investigator-blinded, multicenter study was conducted at 8 United States (US) and 2 Australian sites. All eligible skin lesions were clinically concerning for melanoma, examined with the ESS device, subsequently biopsied according to dermatologists\' standard of care, and evaluated with histopathology. A total of 311 participants with 440 lesions were enrolled, including 44 melanomas (63.6% in situ and 36.4% invasive) and 44 severely dysplastic nevi.
UNASSIGNED: The observed sensitivity of the ESS device for melanoma detection was 95.5% (95% CI, 84.5% to 98.8%, 42 of 44 melanomas), and the observed specificity was 32.5% (95% CI, 27.2% to 38.3%). The positive and negative predictive values were 16.0% and 98.1%, respectively.
UNASSIGNED: The device was tested in a high-risk population with lesions selected for biopsy based on clinical and dermoscopic assessments of board-certified dermatologists. Most enrolled lesions were pigmented.
UNASSIGNED: The ESS device\'s high sensitivity and NPV for the detection of melanoma suggest the device may be a useful adjunctive, point-of-care tool for melanoma detection.