Newborn Screening for Krabbe Disease: Status Quo and Recommendations for Improvements.

Krabbe disease (KD) is part of newborn screening (NBS) in 11 states with at least one additional state preparing to screen. In July 2021, KD was re-nominated for addition to the federal Recommended Uniform Screening Panel (RUSP) in the USA with a two-tiered strategy based on psychosine (PSY) as the determinant if an NBS result is positive or negative after a first-tier test revealed decreased galactocerebrosidase activity. Nine states currently screening for KD include PSY analysis in their screening strategy. However, the nomination was rejected in February 2023 because of perceived concerns about a high false positive rate, potential harm to newborns with an uncertain prognosis, and inadequate data on presymptomatic treatment benefit or harm. To address the concern about false positive NBS results, a survey was conducted of the eight NBS programs that use PSY and have been screening for KD for at least 1 year. Seven of eight states responded. We found that: (1) the use of PSY is variable; (2) when modeling the data based on the recommended screening strategy for KD, and applying different cutoffs for PSY, each state could virtually eliminate false positive results without major impact on sensitivity; (3) the reason for the diverse strategies appears to be primarily the difficulty of state programs to adjust screening algorithms due to the concern of possibly missing even an adult-onset case following a change that focuses on infantile and early infantile KD. Contracts with outside vendors and the effort/cost of making changes to a program's information systems can be additional obstacles. We recommend that programs review their historical NBS outcomes for KD with their advisory committees and make transparent decisions on whether to accept false positive results for such a devastating condition or to adjust their procedures to ensure an efficient, effective, and manageable NBS program for KD.

The heterogeneity effect of surveillance intervals on progression free survival.

Progression-free survival (PFS) is an increasingly important surrogate endpoint in cancer clinical trials. However, the true time of progression is typically unknown if the evaluation of progression status is only scheduled at given surveillance intervals. In addition, comparison between treatment arms under different surveillance schema is not uncommon. Our aim is to explore whether the heterogeneity of the surveillance intervals may interfere with the validity of the conclusion of efficacy based on PFS, and the extent to which the variation would bias the results. We conduct comprehensive simulation studies to explore the aforementioned goals in a two-arm randomized control trial. We introduce three steps to simulate survival data with predefined surveillance intervals under different censoring rate considerations. We report the estimated hazard ratios and examine false positive rate, power and bias under different surveillance intervals, given different baseline median PFS, hazard ratio and censoring rate settings. Results show that larger heterogeneous lengths of surveillance intervals lead to higher false positive rate and overestimate the power, and the effect of the heterogeneous surveillance intervals may depend upon both the life expectancy of the tumor prognoses and the censoring proportion of the survival data. We also demonstrate such heterogeneity effect of surveillance intervals on PFS in a phase III metastatic colorectal cancer trial. In our opinions, adherence to consistent surveillance intervals should be favored in designing the comparative trials. Otherwise, it needs to be appropriately taken into account when analyzing data.

Rethinking False Positive Exercise Electrocardiographic Stress Tests by Assessing Coronary Microvascular Function.

BACKGROUND: Exercise electrocardiographic stress testing (EST) has historically been validated against the demonstration of obstructive coronary artery disease. However, myocardial ischemia can occur because of coronary microvascular dysfunction (CMD) in the absence of obstructive coronary artery disease. OBJECTIVES: The aim of this study was to assess the specificity of EST to detect an ischemic substrate against the reference standard of coronary endothelium-independent and endothelium-dependent microvascular function in patients with angina with nonobstructive coronary arteries (ANOCA). METHODS: Patients with ANOCA underwent invasive coronary physiological assessment using adenosine and acetylcholine. CMD was defined as impaired endothelium-independent and/or endothelium-dependent function. EST was performed using a standard Bruce treadmill protocol, with ischemia defined as the appearance of ≥0.1-mV ST-segment depression 80 ms from the J-point on electrocardiography. The study was powered to detect specificity of ≥91%. RESULTS: A total of 102 patients were enrolled (65% women, mean age 60 ± 8 years). Thirty-two patients developed ischemia (ischemic group) during EST, whereas 70 patients did not (nonischemic group); both groups were phenotypically similar. Ischemia during EST was 100% specific for CMD. Acetylcholine flow reserve was the strongest predictor of ischemia during exercise. Using endothelium-independent and endothelium-dependent microvascular dysfunction as the reference standard, the false positive rate of EST dropped to 0%. CONCLUSIONS: In patients with ANOCA, ischemia on EST was highly specific of an underlying ischemic substrate. These findings challenge the traditional belief that EST has a high false positive rate.

Preoperative predictors of successful tumour localization by intraoperative molecular imaging with pafolacianine in lung cancer to create predictive nomogram.

OBJECTIVES: Intraoperative molecular imaging (IMI) uses cancer-targeted fluorescent probe to locate nodules. Pafolacianine is a Food and Drug Administration-approved fluorescent probe for lung cancer. However, it has a 8-12% false negative rate for localization. Our goal is to define preoperative predictors of tumour localization by IMI. METHODS: We performed a retrospective review of patients who underwent IMI using pafolacianine for lung lesions from June 2015 to August 2019. Candidate predictors including sex, age, body mass index, smoking history, tumour size, distance of tumour from surface, use of neoadjuvant therapy and positron emission tomography avidity were included. The outcome was fluorescence in vivo and comprehensively included those who were true or false positives negatives. Multiple imputation was used to handle the missing data. The final model was evaluated using the area under the receiver operating characteristic curve. RESULTS: Three hundred nine patients were included in our study. The mean age was 64 (standard deviation 13) and 68% had a smoking history. The mean distance of the tumours from the pleural surface was 0.4 cm (standard deviation 0.6). Smoking in pack-years and distance from pleura had an odds ratio of 0.99 [95% confidence interval: 0.98-0.99; P = 0.03] and 0.46 [95% confidence interval: 0.27-0.78; P = 0.004], respectively. The final model had an area under the receiver operating characteristic curve of 0.68 and was used to create a nomogram that gives a probability of fluorescence in vivo. CONCLUSIONS: Primary tumours that are deeper from the pleural surface, especially in patients with a higher pack-years, are associated with a decreased likelihood of intraoperative localization. We identified a nomogram to predict the likelihood of tumour localization with IMI with pafolacianine.

Immunogenicity of Cemiplimab: Low Incidence of Antidrug Antibodies and Cut-Point Suitability Across Tumor Types.

The immunogenicity of cemiplimab, a fully human immunoglobulin G4 monoclonal antibody directed against programmed cell death 1, was assessed in patients across multiple tumor types. The development of antidrug antibodies (ADAs) against cemiplimab was monitored using a validated bridging immunoassay. To identify ADA-positive samples in the assay, statistically determined cut points were established by analyzing baseline clinical study samples from a mixed population of different tumor types, and this validation cut point was used to assess immunogenicity in all subsequent studies. Regulatory guidance requires that ADA assay cut points be verified for appropriateness in different patient populations. Thus, for the cemiplimab ADA assay, we evaluated whether each new oncology population was comparable with the validation population used to set the cut point. Assay responses from 2393 individual serum samples from 8 different tumor types were compared with the validation population, using established statistical methods for cut-point determination and comparison, with no significant differences observed. Across tumor types, the immunogenicity of cemiplimab was low, with an overall treatment-emergent ADA incidence rate of 1.9% and 2.5% at intravenous dose regimens of 3 mg/kg every 2 weeks and 350 mg every 3 weeks, respectively. Moreover, no neutralizing antibodies to cemiplimab were detected in patients with ADA-positive samples, and there was no observed impact of cemiplimab ADAs on pharmacokinetics. Study-specific cut points may be required in some diseases, such as immune and inflammatory diseases; however, based on this analysis, in-study cut points are not required for each new oncology disease indication for cemiplimab.

Clinico-biological-radiomics (CBR) based machine learning for improving the diagnostic accuracy of FDG-PET false-positive lymph nodes in lung cancer.

BACKGROUND: The main problem of positron emission tomography/computed tomography (PET/CT) for lymph node (LN) staging is the high false positive rate (FPR). Thus, we aimed to explore a clinico-biological-radiomics (CBR) model via machine learning (ML) to reduce FPR and improve the accuracy for predicting the hypermetabolic mediastinal-hilar LNs status in lung cancer than conventional PET/CT. METHODS: A total of 260 lung cancer patients with hypermetabolic mediastinal-hilar LNs (SUVmax ≥ 2.5) were retrospectively reviewed. Patients were treated with surgery with systematic LN resection and pathologically divided into the LN negative (LN-) and positive (LN +) groups, and randomly assigned into the training (n = 182) and test (n = 78) sets. Preoperative CBR dataset containing 1738 multi-scale features was constructed for all patients. Prediction models for hypermetabolic LNs status were developed using the features selected by the supervised ML algorithms, and evaluated using the classical diagnostic indicators. Then, a nomogram was developed based on the model with the highest area under the curve (AUC) and the lowest FPR, and validated by the calibration plots. RESULTS: In total, 109 LN- and 151 LN + patients were enrolled in this study. 6 independent prediction models were developed to differentiate LN- from LN + patients using the selected features from clinico-biological-image dataset, radiomics dataset, and their combined CBR dataset, respectively. The DeLong test showed that the CBR Model containing all-scale features held the highest predictive efficiency and the lowest FPR among all of established models (p < 0.05) in both the training and test sets (AUCs of 0.90 and 0.89, FPRs of 12.82% and 6.45%, respectively) (p < 0.05). The quantitative nomogram based on CBR Model was validated to have a good consistency with actual observations. CONCLUSION: This study presents an integrated CBR nomogram that can further reduce the FPR and improve the accuracy of hypermetabolic mediastinal-hilar LNs evaluation than conventional PET/CT in lung cancer, thereby greatly reducing the risk of overestimation and assisting for precision treatment.

Replicate Testing of Clinical Endpoints Can Prevent No-Go Decisions for Beneficial Vaccines.

In vaccine efficacy trials, inaccurate counting of infection cases leads to systematic under-estimation-or "dilution"-of vaccine efficacy. In particular, if a sufficient fraction of observed cases are false positives, apparent efficacy will be greatly reduced, leading to unwarranted no-go decisions in vaccine development. Here, we propose a range of replicate testing strategies to address this problem, considering the additional challenge of uncertainty in both infection incidence and diagnostic assay specificity/sensitivity. A strategy that counts an infection case only if a majority of replicate assays return a positive result can substantially reduce efficacy dilution for assays with non-systematic (i.e., "random") errors. We also find that a cost-effective variant of this strategy, using confirmatory assays only if an initial assay is positive, yields a comparable benefit. In clinical trials, where frequent longitudinal samples are needed to detect short-lived infections, this "confirmatory majority rule" strategy can prevent the accumulation of false positives from magnifying efficacy dilution. When widespread public health screening is used for viruses, such as SARS-CoV-2, that have non-differentiating features or may be asymptomatic, these strategies can also serve to reduce unneeded isolations caused by false positives.

Analysis of retest reliability for pregnant women undergoing cfDNA testing with a no-call result.

BACKGROUND: Determining the reasons for unreportable or no-call cell-free DNA (cfDNA) test results has been an ongoing issue, and a consensus on subsequent management is still lacking. This study aimed to explore potential factors related to no-call cfDNA test results and to discuss whether retest results are reliable. METHODS AND RESULTS: This was a retrospective study of women with singleton pregnancies undergoing cfDNA testing in 2021. Of the 9871 pregnant patients undergoing cfDNA testing, 111 had a no-call result, and their results were compared to those of 170 control patients. The no-call rate was 1.12% (111/9871), and the primary cause for no-call results was data fluctuation (88.29%, 98/111). Medical conditions were significantly more frequent in the no-call group than in the reportable results group (P < 0.001). After retesting, 107 (107/111, 96.40%) patients had a result, and the false-positive rate (FPR) of retesting was 10.09% (10.09%, 11/109). In addition, placental lesions were more frequent in the no-call group than in the reportable results group (P = 0.037), and 4 patients, all in the no-call group, experienced pregnancy loss. CONCLUSIONS: Pregnant women with medical conditions are more likely to have a no-call result. A retest is suggested for patients with a no-call result, but retests have a high FPR. In addition, pregnant women with a no-call result are at increased risk of adverse pregnancy outcomes. In conclusion, more attention should be given to pregnant women for whom a no-call cfDNA result is obtained.

Prenatal screening for preeclampsia: the roles of placental growth factor and pregnancy-associated plasma protein A in the first trimester and placental growth factor and soluble fms-like tyrosine kinase 1-placental growth factor ratio in the early second trimester.

BACKGROUND: Professional societies have recommended universal first trimester screening for preeclampsia and a second or third trimester soluble fms-like tyrosine kinase-1-placental growth factor ratio test to assess for preeclampsia and its severity. However, it may not be feasible to implement the most optimal screening protocol for preeclampsia in the first trimester which uses a combination of maternal characteristics, maternal biophysical and biochemical markers due to limitations in the access to uterine artery doppler ultrasound. There are inconsistent findings on how early in the second trimester the fms-like tyrosine kinase-1-placental growth factor ratio begins to provide useful information in preeclampsia prediction. OBJECTIVE: This study aimed to assess the accuracy of (1) a combination of maternal characteristics, maternal serum pregnancy-associated plasma protein A, and placental growth factor in the screening for preeclampsia in the first trimester; and (2) placental growth factor or soluble fms-like tyrosine kinase-1-placental growth factor ratio in the prediction of preeclampsia in the early second trimester. STUDY DESIGN: This retrospective case-control study used frozen residual blood samples from women who had aneuploidy screening and delivered at a tertiary center. The case group included pregnancies with gestational hypertension or preeclampsia (further classified as early-onset [birth at <34 weeks' gestation] and preterm preeclampsia [birth at <37 weeks' gestation]). Each case was matched with 3 control pregnancies by date of blood sample draw, gestational age at first blood sample draw, maternal age, maternal ethnicity, type of multiple-marker screening, and amount of residual sample. Mann-Whitney U tests were used to assess the associations between serum markers and the risk of preeclampsia. Logistic regressions were used to assess if the risk of preeclampsia can be predicted using a combination of maternal characteristics and serum markers. RESULTS: The case group included 146 preeclampsia and 295 gestational hypertension cases. Compared with the controls, preeclampsia cases had significantly lower first-trimester pregnancy-associated plasma protein A and placental growth factor. At a 20% false-positive rate, 71% of early-onset and 58% of preterm preeclampsia cases can be predicted using maternal characteristics, pregnancy-associated plasma protein A, and placental growth factor. Preeclampsia cases had lower second-trimester placental growth factor and a higher soluble fms-like tyrosine kinase-1-placental growth factor ratio. At a 10% false-positive rate, 80% and 53% of early-onset preeclampsia can be predicted using maternal characteristics and placental growth factor or soluble fms-like tyrosine kinase-1-placental growth factor ratio, respectively. CONCLUSION: The current first-trimester aneuploidy screening programs may be expanded to identify women at increased risk of developing preeclampsia. Early in the second trimester, placental growth factor alone provided better prediction for preeclampsia compared with the soluble fms-like tyrosine kinase-1-placental growth factor ratio.

A novel principal component based method for identifying differentially methylated regions in Illumina Infinium MethylationEPIC BeadChip data.

Differentially methylated regions (DMRs) are genomic regions with methylation patterns across multiple CpG sites that are associated with a phenotype. In this study, we proposed a Principal Component (PC) based DMR analysis method for use with data generated using the Illumina Infinium MethylationEPIC BeadChip (EPIC) array. We obtained methylation residuals by regressing the M-values of CpGs within a region on covariates, extracted PCs of the residuals, and then combined association information across PCs to obtain regional significance. Simulation-based genome-wide false positive (GFP) rates and true positive rates were estimated under a variety of conditions before determining the final version of our method, which we have named DMRPC. Then, DMRPC and another DMR method, coMethDMR, were used to perform epigenome-wide analyses of several phenotypes known to have multiple associated methylation loci (age, sex, and smoking) in a discovery and a replication cohort. Among regions that were analysed by both methods, DMRPC identified 50% more genome-wide significant age-associated DMRs than coMethDMR. The replication rate for the loci that were identified by only DMRPC was higher than the rate for those that were identified by only coMethDMR (90% for DMRPC vs. 76% for coMethDMR). Furthermore, DMRPC identified replicable associations in regions of moderate between-CpG correlation which are typically not analysed by coMethDMR. For the analyses of sex and smoking, the advantage of DMRPC was less clear. In conclusion, DMRPC is a new powerful DMR discovery tool that retains power in genomic regions with moderate correlation across CpGs.

Gaussian Mixture Modeling Extensions for Improved False Discovery Rate Estimation in GC-MS Metabolomics.

The ability to reliably identify small molecules (e.g., metabolites) is key toward driving scientific advancement in metabolomics. Gas chromatography-mass spectrometry (GC-MS) is an analytic method that may be applied to facilitate this process. The typical GC-MS identification workflow involves quantifying the similarity of an observed sample spectrum and other features (e.g., retention index) to that of several references, noting the compound of the best-matching reference spectrum as the identified metabolite. While a deluge of similarity metrics exist, none quantify the error rate of generated identifications, thereby presenting an unknown risk of false identification or discovery. To quantify this unknown risk, we propose a model-based framework for estimating the false discovery rate (FDR) among a set of identifications. Extending a traditional mixture modeling framework, our method incorporates both similarity score and experimental information in estimating the FDR. We apply these models to identification lists derived from across 548 samples of varying complexity and sample type (e.g., fungal species, standard mixtures, etc.), comparing their performance to that of the traditional Gaussian mixture model (GMM). Through simulation, we additionally assess the impact of reference library size on the accuracy of FDR estimates. In comparing the best performing model extensions to the GMM, our results indicate relative decreases in median absolute estimation error (MAE) ranging from 12% to 70%, based on comparisons of the median MAEs across all hit-lists. Results indicate that these relative performance improvements generally hold despite library size; however FDR estimation error typically worsens as the set of reference compounds diminishes.

Accuracy of placental growth factor alone or in combination with soluble fms-like tyrosine kinase-1 or maternal factors in detecting preeclampsia in asymptomatic women in the second and third trimesters: a systematic review and meta-analysis.

OBJECTIVE: This study aimed to: (1) identify all relevant studies reporting on the diagnostic accuracy of maternal circulating placental growth factor) alone or as a ratio with soluble fms-like tyrosine kinase-1), and of placental growth factor-based models (placental growth factor combined with maternal factors±other biomarkers) in the second or third trimester to predict subsequent development of preeclampsia in asymptomatic women; (2) estimate a hierarchical summary receiver-operating characteristic curve for studies reporting on the same test but different thresholds, gestational ages, and populations; and (3) select the best method to screen for preeclampsia in asymptomatic women during the second and third trimester of pregnancy by comparing the diagnostic accuracy of each method. DATA SOURCES: A systematic search was performed through MEDLINE, Embase, CENTRAL, ClinicalTrials.gov, and the World Health Organization International Clinical Trials Registry Platform databases from January 1, 1985 to April 15, 2021. STUDY ELIGIBILITY CRITERIA: Studies including asymptomatic singleton pregnant women at >18 weeks' gestation with risk of developing preeclampsia were evaluated. We included only cohort or cross-sectional test accuracy studies reporting on preeclampsia outcome, allowing tabulation of 2×2 tables, with follow-up available for >85%, and evaluating performance of placental growth factor alone, soluble fms-like tyrosine kinase-1- placental growth factor ratio, or placental growth factor-based models. The study protocol was registered on the International Prospective Register Of Systematic Reviews (CRD 42020162460). METHODS: Because of considerable intra- and interstudy heterogeneity, we computed the hierarchical summary receiver-operating characteristic plots and derived diagnostic odds ratios, ß, θi, and Λ for each method to compare performances. The quality of the included studies was evaluated by the QUADAS-2 tool. RESULTS: The search identified 2028 citations, from which we selected 474 studies for detailed assessment of the full texts. Finally, 100 published studies met the eligibility criteria for qualitative and 32 for quantitative syntheses. Twenty-three studies reported on performance of placental growth factor testing for the prediction of preeclampsia in the second trimester, including 16 (with 27 entries) that reported on placental growth factor test alone, 9 (with 19 entries) that reported on the soluble fms-like tyrosine kinase-1-placental growth factor ratio, and 6 (16 entries) that reported on placental growth factor-based models. Fourteen studies reported on performance of placental growth factor testing for the prediction of preeclampsia in the third trimester, including 10 (with 18 entries) that reported on placental growth factor test alone, 8 (with 12 entries) that reported on soluble fms-like tyrosine kinase-1-placental growth factor ratio, and 7 (with 12 entries) that reported on placental growth factor-based models. For the second trimester, Placental growth factor-based models achieved the highest diagnostic odds ratio for the prediction of early preeclampsia in the total population compared with placental growth factor alone and soluble fms-like tyrosine kinase-1-placental growth factor ratio (placental growth factor-based models, 63.20; 95% confidence interval, 37.62-106.16 vs soluble fms-like tyrosine kinase-1-placental growth factor ratio, 6.96; 95% confidence interval, 1.76-27.61 vs placental growth factor alone, 5.62; 95% confidence interval, 3.04-10.38); placental growth factor-based models had higher diagnostic odds ratio than placental growth factor alone for the identification of any-onset preeclampsia in the unselected population (28.45; 95% confidence interval, 13.52-59.85 vs 7.09; 95% confidence interval, 3.74-13.41). For the third trimester, Placental growth factor-based models achieved prediction for any-onset preeclampsia that was significantly better than that of placental growth factor alone but similar to that of soluble fms-like tyrosine kinase-1-placental growth factor ratio (placental growth factor-based models, 27.12; 95% confidence interval, 21.67-33.94 vs placental growth factor alone, 10.31; 95% confidence interval, 7.41-14.35 vs soluble fms-like tyrosine kinase-1-placental growth factor ratio, 14.94; 95% confidence interval, 9.42-23.70). CONCLUSION: Placental growth factor with maternal factors ± other biomarkers determined in the second trimester achieved the best predictive performance for early preeclampsia in the total population. However, in the third trimester, placental growth factor-based models had predictive performance for any-onset preeclampsia that was better than that of placental growth factor alone but similar to that of soluble fms-like tyrosine kinase-1-placental growth factor ratio. Through this meta-analysis, we have identified a large number of very heterogeneous studies. Therefore, there is an urgent need to develop standardized research using the same models that combine serum placental growth factor with maternal factors ± other biomarkers to accurately predict preeclampsia. Identification of patients at risk might be beneficial for intensive monitoring and timing delivery.

Big little lies: a compendium and simulation of p-hacking strategies.

In many research fields, the widespread use of questionable research practices has jeopardized the credibility of scientific results. One of the most prominent questionable research practices is p-hacking. Typically, p-hacking is defined as a compound of strategies targeted at rendering non-significant hypothesis testing results significant. However, a comprehensive overview of these p-hacking strategies is missing, and current meta-scientific research often ignores the heterogeneity of strategies. Here, we compile a list of 12 p-hacking strategies based on an extensive literature review, identify factors that control their level of severity, and demonstrate their impact on false-positive rates using simulation studies. We also use our simulation results to evaluate several approaches that have been proposed to mitigate the influence of questionable research practices. Our results show that investigating p-hacking at the level of strategies can provide a better understanding of the process of p-hacking, as well as a broader basis for developing effective countermeasures. By making our analyses available through a Shiny app and R package, we facilitate future meta-scientific research aimed at investigating the ramifications of p-hacking across multiple strategies, and we hope to start a broader discussion about different manifestations of p-hacking in practice.

Korean Red Ginseng slows coreceptor switch in HIV-1 infected patients.

Background: Human immunodeficiency virus-1 (HIV-1) that binds to the coreceptor CCR5 (R5 viruses) can evolve into viruses that bind to the coreceptor CXCR4 (X4 viruses), with high viral replication rates governing this coreceptor switch. Korean Red Ginseng (KRG) treatment of HIV-1 infected patients has been found to slow the depletion of CD4+ T cells. This study assessed whether the KRG-associated slow depletion of CD4+ T cells was associated with coreceptor switching. Methods: This study included 146 HIV-1-infected patients naïve to antiretroviral therapy (ART) and seven patients receiving ART. A total of 540 blood samples were obtained from these patients over 122 ± 129 months. Their env genes were amplified by nested PCR or RT-PCR and subjected to direct sequencing. Tropism was determined with a 10% false positive rate (FPR) cutoff. Results: Of the 146 patients naïve to ART, 102 were KRG-naïve, and 44 had been treated with KRG. Evaluation of initial samples showed that coreceptor switch had occurred in 19 patients, later occurring in 38 additional patients. There was a significant correlation between the amount of KRG and FPR. Based on initial samples, the R5 maintenance period was extended 2.35-fold, with the coreceptor switch being delayed 2.42-fold in KRG-treated compared with KRG-naïve patients. The coreceptor switch occurred in 85% of a homogeneous cohort. The proportion of patients who maintained R5 for ≥10 years was significantly higher in long-term slow progressors than in typical progressors. Conclusion: KRG therapy extends R5 maintenance period by increasing FPR, thereby slowing the coreceptor switch.

Allergy to Local Anesthetics is a Rarity: Review of Diagnostics and Strategies for Clinical Management.

Local anesthetics (LA) are commonly used in procedures and in topical agents for pain management. With the increasing use of LA drugs, the management of LA reactions is more frequently encountered in the office and in operating rooms. True allergic reactions involving IgE-mediated reactions and anaphylaxis are rare; they have only been identified in case reports and account for less than 1% of adverse LA reactions. Most reactions are non-allergic or are a result of hypersensitivity to other culprits such as preservatives, excipients, or other exposures. LA reactions that are misclassified as true allergies can lead to unnecessary avoidance of LA drugs or delays in surgical procedures that require their use. A detailed history of prior LA reactions is the first and most crucial step for understanding the nature of the reaction. Reactions that are suspicious for an immediate hypersensitivity reaction can be evaluated with skin prick and intradermal testing with subsequent graded challenge. Reactions that are suspicious for a delayed hypersensitivity reaction can be evaluated with patch testing.

TransDFL: Identification of Disordered Flexible Linkers in Proteins by Transfer Learning.

Disordered flexible linkers (DFLs) are the functional disordered regions in proteins, which are the sub-regions of intrinsically disordered regions (IDRs) and play important roles in connecting domains and maintaining inter-domain interactions. Trained with the limited available DFLs, the existing DFL predictors based on the machine learning techniques tend to predict the ordered residues as DFLs, leading to a high falsepositive rate (FPR) and low prediction accuracy. Previous studies have shown that DFLs are extremely flexible disordered regions, which are usually predicted as disordered residues with high confidence [P(D) > 0.9] by an IDR predictor. Therefore, transferring an IDR predictor to an accurate DFL predictor is of great significance for understanding the functions of IDRs. In this study, we proposed a new predictor called TransDFL for identifying DFLs by transferring the RFPR-IDP predictor for IDR identification to the DFL prediction. The RFPR-IDP was pre-trained with IDR sequences to learn the general features between IDRs and DFLs, which is helpful to reduce the false positives in the ordered regions. RFPR-IDP was fine-tuned with the DFL sequences to capture the specific features of DFLs so as to be transferred into the TransDFL. Experimental results of two application scenarios (prediction of DFLs only in IDRs or prediction of DFLs in entire proteins) showed that TransDFL consistently outperformed other existing DFL predictors with higher accuracy. The corresponding web server of TransDFL can be freely accessed at http://bliulab.net/TransDFL/.

HPIPred: Host-pathogen interactome prediction with phenotypic scoring.

Protein-protein interactions (PPIs) are involved in most cellular processes. Unfortunately, current knowledge of host-pathogen interactomes is still very limited. Experimental methods used to detect PPIs have several limitations, including increasing complexity and economic cost in large-scale screenings. Hence, computational methods are commonly used to support experimental data, although they generally suffer from high false-positive rates. To address this issue, we have created HPIPred, a host-pathogen PPI prediction tool based on numerical encoding of physicochemical properties. Unlike other available methods, HPIPred integrates phenotypic data to prioritize biologically meaningful results. We used HPIPred to screen the entire Homo sapiens and Pseudomonas aeruginosa PAO1 proteomes to generate a host-pathogen interactome with 763 interactions displaying a highly connected network topology. Our predictive model can be used to prioritize protein-protein interactions as potential targets for antibacterial drug development. Available at: https://github.com/SysBioUAB/hpi_predictor.

Racial differences in positive findings on embedded performance validity tests.

INTRODUCTION: Embedded performance validity tests (PVTs) may show increased positive findings in racially diverse examinees. This study examined positive findings in an older adult sample of African American (AA) and European American (EA) individuals recruited as part of a study on aging and cognition. METHOD: The project involved secondary analysis of deidentified National Alzheimer's Coordinating Center data (N = 22,688). Exclusion criteria included diagnosis of dementia (n = 5,550), mild cognitive impairment (MCI; n = 5,160), impaired but not MCI (n = 1,126), other race (n = 864), and abnormal Mini Mental State Examination (MMSE < 25; n = 135). The initial sample included 9,853 participants (16.4% AA). Propensity score matching matched AA and EA participants on age, education, sex, and MMSE score. The final sample included 3,024 individuals with 50% of participants identifying as AA. Premorbid ability estimates were calculated based on demographics. Failure rates on five raw score and six age-adjusted scaled score PVTs were examined by race. RESULTS: Age, education, sex, MMSE, and premorbid ability estimate were not significantly different by race. Thirteen percent of AA and 3.8% of EA participants failed two or more raw score PVTs (p < .0001). On age-adjusted PVTs, 20.6% of AA and 5.9% of EA participants failed two or more (p < .0001). CONCLUSIONS: PVT failure rates were significantly higher among AA participants. Findings indicate a need for cautious interpretation of embedded PVTs with underrepresented groups. Adjustments to embedded PVT cutoffs may need to be considered to improve diagnostic accuracy.

Surveillance of SARS-CoV-2 Antibodies in Early 2020 among Rhode Island Pregnancies.

OBJECTIVE: Limited data are available on the performance of SARS-CoV-2 antibody assays and data collected during pregnancy vary widely. The objective of this study was to estimate the seroprevalence of antibodies against SARS-CoV-2 in pregnant individuals in Rhode Island and to evaluate whether the prevalence differed by month of collection, age, county of residence, or economic status as estimated by zip code. METHODS: Pre-pandemic (2019) and early pandemic (2020) serum samples, collected for prenatal screening between 15 and 22 weeks of gestation, were analyzed utilizing two SARS-CoV-2 immunoglobulin G (IgG) automated assays that targeted the viral nucleocapsid (anti-N) or spike (anti-S) receptor binding domain proteins. RESULTS: Among 756 pre-pandemic samples, one anti-S IgG and 13 anti-N IgG were identified. No samples were positive for both. Among 787 pandemic specimens, 16 (2.03%) were positive for both anti-N IgG and anti-S IgG. When stratified by month of collection, there was a significant increase in seropositivity rate (p=0.023). Seropositivity rates were associated with lower income levels (p=0.08) but this was not statistically significant. No trend by maternal age was found (p=0.70). CONCLUSIONS: When a positive result was defined as both anti-N IgG and anti-S IgG, false positives were unlikely. Based on this methodology, serology could be utilized to monitor infection trends during pregnancy.