A CT-based Deep Learning Model for Predicting Subsequent Fracture Risk in Patients with Hip Fracture.

Background Patients have the highest risk of subsequent fractures in the first few years after an initial fracture, yet models to predict short-term subsequent risk have not been developed. Purpose To develop and validate a deep learning prediction model for subsequent fracture risk using digitally reconstructed radiographs from hip CT in patients with recent hip fractures. Materials and Methods This retrospective study included adult patients who underwent three-dimensional hip CT due to a fracture from January 2004 to December 2020. Two-dimensional frontal, lateral, and axial digitally reconstructed radiographs were generated and assembled to construct an ensemble model. DenseNet modules were used to calculate risk probability based on extracted image features and fracture-free probability plots were output. Model performance was assessed using the C index and area under the receiver operating characteristic curve (AUC) and compared with other models using the paired t test. Results The training and validation set included 1012 patients (mean age, 74.5 years ± 13.3 [SD]; 706 female, 113 subsequent fracture) and the test set included 468 patients (mean age, 75.9 years ± 14.0; 335 female, 22 subsequent fractures). In the test set, the ensemble model had a higher C index (0.73) for predicting subsequent fractures than that of other image-based models (C index range, 0.59-0.70 for five of six models; P value range, < .001 to < .05). The ensemble model achieved AUCs of 0.74, 0.74, and 0.73 at the 2-, 3-, and 5-year follow-ups, respectively; higher than that of most other image-based models at 2 years (AUC range, 0.57-0.71 for five of six models; P value range, < .001 to < .05) and 3 years (AUC range, 0.55-0.72 for four of six models; P value range, < .001 to < .05). Moreover, the AUCs achieved by the ensemble model were higher than that of a clinical model that included known risk factors (2-, 3-, and 5-year AUCs of 0.58, 0.64, and 0.70, respectively; P < .001 for all). Conclusion In patients with recent hip fractures, the ensemble deep learning model using digital reconstructed radiographs from hip CT showed good performance for predicting subsequent fractures in the short term. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Li and Jaremko in this issue.

CRYAB stop-loss variant causes rare syndromic dilated cardiomyopathy with congenital cataract: expanding the phenotypic and mutational spectrum of alpha-B crystallinopathy.

Missense mutations in the alpha-B crystallin gene (CRYAB) have been reported in desmin-related myopathies with or without cardiomyopathy and have also been reported in families with only a cataract phenotype. Dilated cardiomyopathy (DCM) is a disorder with a highly heterogeneous genetic etiology involving more than 60 causative genes, hindering genetic diagnosis. In this study, we performed whole genome sequencing on 159 unrelated patients with DCM and identified an unusual stop-loss pathogenic variant in NM_001289808.2:c.527A>G of CRYAB in one patient. The mutant alpha-B crystallin protein is predicted to have an extended strand with addition of 19 amino acid residues, p.(Ter176TrpextTer19), which may contribute to aggregation and increased hydrophobicity of alpha-B crystallin. The proband, diagnosed with DCM at age 32, had a history of bilateral congenital cataracts but had no evidence of myopathy or associated symptoms. He also has a 10-year-old child diagnosed with bilateral congenital cataracts with the same CRYAB variant. This study expands the mutational spectrum of CRYAB and deepens our understanding of the complex phenotypes of alpha-B crystallinopathies.

Clinical feature, GALC variant spectrum, and genotype-phenotype correlation in Korean Krabbe disease patients: Multicenter experience over 13 years.

Krabbe disease (KD) is an autosomal recessive neurodegenerative disorder caused by deficiency of the galactocerebrosidase (GALC) due to variants in the GALC gene. Here, we provide the first and the largest comprehensive analysis of clinical and genetic characteristics, and genotype-phenotype correlations of KD in Korean in comparison with other ethnic groups. From June 2010 to June 2023, 10 patients were diagnosed with KD through sequencing of GALC. Clinical features, and results of GALC sequencing, biochemical test, neuroimaging, and neurophysiologic test were obtained from medical records. An additional nine previously reported Korean KD patients were included for review. In Korean KD patients, the median age of onset was 2 years (3 months-34 years) and the most common phenotype was adult-onset (33%, 6/18) KD, followed by infantile KD (28%, 5/18). The most frequent variants were c.683_694delinsCTC (23%) and c.1901T>C (23%), while the 30-kb deletion was absent. Having two heterozygous pathogenic missense variants was associated with later-onset phenotype. Clinical features were similar to those of other ethnic groups. In Korean KD patients, the most common phenotype was the adult-onset type and the GALC variant spectrum was different from that of the Caucasian population. This study would further our understanding of KD.

Overcoming challenges associated with identifying FBN1 deep intronic variants through whole-genome sequencing.

BACKGROUND: Marfan syndrome (MFS), caused by pathogenic variants of FBN1 (fibrillin-1), is a systemic connective tissue disorder with variable phenotypes and treatment responsiveness depending on the variant. However, a significant number of individuals with MFS remain genetically unexplained. In this study, we report novel pathogenic intronic variants in FBN1 in two unrelated families with MFS. METHODS: We evaluated subjects with suspected MFS from two unrelated families using Sanger sequencing or multiplex ligation-dependent probe amplification of FBN1 and/or panel-based next-generation sequencing. As no pathogenic variants were identified, whole-genome sequencing was performed. Identified variants were analyzed by reverse transcription-PCR and targeted sequencing of FBN1 mRNA harvested from peripheral blood or skin fibroblasts obtained from affected probands. RESULTS: We found causative deep intronic variants, c.6163+1484A>T and c.5788+36C>A, in FBN1. The splicing analysis revealed an insertion of in-frame or out-of-frame intronic sequences of the FBN1 transcript predicted to alter function of calcium-binding epidermal growth factor protein domain. Family members carrying c.6163+1484A>T had high systemic scores including prominent skeletal features and aortic dissection with lesser aortic dilatation. Family members carrying c.5788+36C>A had more severe aortic root dilatation without aortic dissection. Both families had ectopia lentis. CONCLUSION: Variable penetrance of the phenotype and negative genetic testing in MFS families should raise the possibility of deep intronic FBN1 variants and the need for additional molecular studies. This study expands the mutation spectrum of FBN1 and points out the importance of intronic sequence analysis and the need for integrative functional studies in MFS diagnosis.

Fully automatic volume measurement of the adrenal gland on CT using deep learning to classify adrenal hyperplasia.

OBJECTIVES: To develop a fully automated deep learning model for adrenal segmentation and to evaluate its performance in classifying adrenal hyperplasia. METHODS: This retrospective study evaluated automated adrenal segmentation in 308 abdominal CT scans from 48 patients with adrenal hyperplasia and 260 patients with normal glands from 2010 to 2021 (mean age, 42 years; 156 women). The dataset was split into training, validation, and test sets at a ratio of 6:2:2. Contrast-enhanced CT images and manually drawn adrenal gland masks were used to develop a U-Net-based segmentation model. Predicted adrenal volumes were obtained by fivefold splitting of the dataset without overlapping the test set. Adrenal volumes and anthropometric parameters (height, weight, and sex) were utilized to develop an algorithm to classify adrenal hyperplasia, using multilayer perceptron, support vector classification, a random forest classifier, and a decision tree classifier. To measure the performance of the developed model, the dice coefficient and intraclass correlation coefficient (ICC) were used for segmentation, and area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used for classification. RESULTS: The model for segmenting adrenal glands achieved a Dice coefficient of 0.7009 for 308 cases and an ICC of 0.91 (95% CI, 0.90-0.93) for adrenal volume. The models for classifying hyperplasia had the following results: AUC, 0.98-0.99; accuracy, 0.948-0.961; sensitivity, 0.750-0.813; and specificity, 0.973-1.000. CONCLUSION: The proposed segmentation algorithm can accurately segment the adrenal glands on CT scans and may help clinicians identify possible cases of adrenal hyperplasia. KEY POINTS: • A deep learning segmentation method can accurately segment the adrenal gland, which is a small organ, on CT scans. • The machine learning algorithm to classify adrenal hyperplasia using adrenal volume and anthropometric parameters (height, weight, and sex) showed good performance. • The proposed segmentation algorithm may help clinicians identify possible cases of adrenal hyperplasia.

The Diagnostic Yield and Difficulties of Utilizing Soft-clipped Read Clusters Encountered in Clinical Exome Sequencing.

BACKGROUND: Despite the wide use of next generation sequencing, there are still many difficulties in detecting structural variants. A split read is one of the clues of structural variants and is represented as a soft-clipped read in the raw sequencing data. Considering that most of the breakpoints of structural variants reside in non-coding regions, split read information has not been routinely used in exome sequencing or targeted panel sequencing. Recently, SCRAMble, a software capable of detecting mobile element insertion (MEI) and deletion based on soft-clipped read clusters (SCRCs), was shown to provide an additional diagnostic yield of 0.03 - 0.25%. SCRAMble is the only software that can be used for exome sequencing or targeted panel sequencing to detect structural variants based on SCRC information. The aim of present study was to establish a working procedure of utilizing SCRC information using SCRAMble in clinical exome sequencing and to assess its diagnostic yield. METHODS: Raw sequencing data of clinical exome sequencing were retrospectively analyzed using SCRAMble to search MEIs and deletions. SCRAMble software was installed according to the manufacturer's instructions and default parameters except for one, mei-score, which was adjusted for sensitivity, were used. RefSeq gene annotation was performed for both MEI and deletion calls using ANNOVAR. Blacklist-based filtering was used to reduce candidate MEI/deletion calls. Clinical relevance was manually evaluated for the remaining variant calls. RESULTS: One diagnostic MEI, which is a founder variant in East Asia, was detected in two cases (2/266, 0.75%). In addition, two diagnostic deletions, which had been previously detected in depth-of-coverage (DOC)-based copy number variant (CNV) callers, were detected (2/266, 0.75%). Base-level breakpoints that could not be derived by the DOC-based callers were identified for these two deletions using SCRAMble. Most SCRCs were repetitive among cases and blacklist-based filtering reduced candidate MEI/deletion calls by 49.5 - 94.5%, leaving a considerable number of variant calls to be manually validated. CONCLUSIONS: SCRC screening in exome or targeted panel sequencing may provide additional diagnostic yield either by pathogenic MEI detection or reassurance of deletions identified by DOC-based CNV callers. Development of an efficient filtering algorithm is warranted.

Preliminary analysis of predicting the first recurrence in patients with neovascular age-related macular degeneration using deep learning.

BACKGROUND: To predict, using deep learning, the first recurrence in patients with neovascular age-related macular degeneration (nAMD) after three monthly loading injections of intravitreal anti-vascular endothelial growth factor (anti-VEGF). METHODS: Optical coherence tomography (OCT) images were obtained at baseline and after the loading phase. The first recurrence was defined as the initial appearance of a new retinal hemorrhage or intra/subretinal fluid accumulation after the initial resolution of exudative changes after three loading injections. Standard U-Net architecture was used to identify the three retinal fluid compartments, which include pigment epithelial detachment, subretinal fluid, and intraretinal fluid. To predict the first recurrence of nAMD, classification learning was conducted to determine whether the first recurrence occurred within three months after the loading phase. The recurrence classification architecture was built using ResNet50. The model with retinal regions of interest of the entire region and fluid region on OCT at baseline and after the loading phase is presented. RESULTS: A total of 1,444 eyes of 1,302 patients were included. The mean duration until the first recurrence after the loading phase was 8.20 ± 15.56 months. The recurrence classification system revealed that the model with the fluid region of OCT after the loading phase provided the highest classification performance, with an area under the receiver operating characteristic curve (AUC) of 0.725 ± 0.012. Heatmap analysis revealed that three pathological fluids, subsided choroidal neovascularization lesions, and hyperreflective foci were important areas for the first recurrence. CONCLUSIONS: The deep learning algorithm allowed for the prediction of the first recurrence for three months after the loading phase with adequate feasibility. An automated prediction system may assist in establishing patient-specific treatment plans and the provision of individualized medical care for patients with nAMD.

Analysis of MicroRNA Signature Differentially Expressed in Pancreatic Islet Cells Treated with Pancreatic Cancer-Derived Exosomes.

Since the majority of patients with pancreatic cancer (PC) develop insulin resistance and/or diabetes mellitus (DM) prior to PC diagnosis, PC-induced diabetes mellitus (PC-DM) has been a focus for a potential platform for PC detection. In previous studies, the PC-derived exosomes were shown to contain the mediators of PC-DM. In the present study, the response of normal pancreatic islet cells to the PC-derived exosomes was investigated to determine the potential biomarkers for PC-DM, and consequently, for PC. Specifically, changes in microRNA (miRNA) expression were evaluated. The miRNA specimens were prepared from the untreated islet cells as well as the islet cells treated with the PC-derived exosomes (from 50 patients) and the healthy-derived exosomes (from 50 individuals). The specimens were subjected to next-generation sequencing and bioinformatic analysis to determine the differentially expressed miRNAs (DEmiRNAs) only in the specimens treated with the PC-derived exosomes. Consequently, 24 candidate miRNA markers, including IRS1-modulating miRNAs such as hsa-miR-144-5p, hsa-miR-3148, and hsa-miR-3133, were proposed. The proposed miRNAs showed relevance to DM and/or insulin resistance in a literature review and pathway analysis, indicating a potential association with PC-DM. Due to the novel approach used in this study, additional evidence from future studies could corroborate the value of the miRNA markers discovered.

Intronic LINE-1 insertion in SLCO1B3 as a highly prevalent cause of rotor syndrome in East Asian population.

Rotor syndrome is caused by digenic loss-of-function variants in SLCO1B1 and SLCO1B3 but only a few studies have reported co-occurring inactivating variants from both genes. A rotor syndrome-causing long interspersed element-1 (LINE-1) insertion in SLCO1B3 had been reported to be highly prevalent in the Japanese population but there has been no additional report. In spite of its known association with various human diseases, LINE-1 is hard to detect with current sequencing technologies. In this study, we aimed to devise a method to screen the LINE-1 insertion variant and investigate the frequency of this variant in various populations. A chimeric sequence, that was generated by concatenating the reference sequence at the junction and a part of inserted LINE-1 sequence, was searched from 725 raw sequencing data files. In cases containing the chimeric sequence, confirmatory long-range PCR and gap-PCR were performed. In total, 95 (13.1%) of 725 patients were positive for the chimeric sequence, and all were confirmed to have the SLCO1B3 LINE-1 insertion by PCR-based tests. The same chimeric sequence was searched from the 1000 Genomes Project data repository and the carrier frequency was remarkably high in the East Asian populations (10.1%), especially in Southern Han Chinese (18.5%), but almost absent in other populations. This SLCO1B3 LINE-1 insertion should be screened in a population-specific manner under suspicion of Rotor syndrome and the methods proposed in this study would enable this in a simple way.

Longitudinal proteomic profiling provides insights into host response and proteome dynamics in COVID-19 progression.

In managing patients with coronavirus disease 2019 (COVID-19), early identification of those at high risk and real-time monitoring of disease progression to severe COVID-19 is a major challenge. We aimed to identify potential early prognostic protein markers and to expand understanding of proteome dynamics during clinical progression of the disease. We performed in-depth proteome profiling on 137 sera, longitudinally collected from 25 patients with COVID-19 (non-severe patients, n = 13; patients who progressed to severe COVID-19, n = 12). We identified 11 potential biomarkers, including the novel markers IGLV3-19 and BNC2, as early potential prognostic indicators of severe COVID-19. These potential biomarkers are mainly involved in biological processes associated with humoral immune response, interferon signalling, acute phase response, lipid metabolism, and platelet degranulation. We further revealed that the longitudinal changes of 40 proteins persistently increased or decreased as the disease progressed to severe COVID-19. These 40 potential biomarkers could effectively reflect the clinical progression of the disease. Our findings provide some new insights into host response to SARS-CoV-2 infection, which are valuable for understanding of COVID-19 disease progression. This study also identified potential biomarkers that could be further validated, which may support better predicting and monitoring progression to severe COVID-19.

Evidence of Severe Acute Respiratory Syndrome Coronavirus 2 Reinfection After Recovery from Mild Coronavirus Disease 2019.

BACKGROUND: Positive results from real-time reverse-transcription polymerase chain reaction (rRT-PCR) in recovered patients raise concern that patients who recover from coronavirus disease 2019 (COVID-19) may be at risk of reinfection. Currently, however, evidence that supports reinfection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has not been reported. METHODS: We conducted whole-genome sequencing of the viral RNA from clinical specimens at the initial infection and at the positive retest from 6 patients who recovered from COVID-19 and retested positive for SARS-CoV-2 via rRT-PCR after recovery. A total of 13 viral RNAs from the patients' respiratory specimens were consecutively obtained, which enabled us to characterize the difference in viral genomes between initial infection and positive retest. RESULTS: At the time of the positive retest, we were able to acquire a complete genome sequence from patient 1, a 21-year-old previously healthy woman. In this patient, through the phylogenetic analysis, we confirmed that the viral RNA of positive retest was clustered into a subgroup distinct from that of the initial infection, suggesting that there was a reinfection of SARS-CoV-2 with a subtype that was different from that of the primary strain. The spike protein D614G substitution that defines the clade "G" emerged in reinfection, while mutations that characterize the clade "V" (ie, nsp6 L37F and ORF3a G251V) were present at initial infection. CONCLUSIONS: Reinfection with a genetically distinct SARS-CoV-2 strain may occur in an immunocompetent patient shortly after recovery from mild COVID-19. SARS-CoV-2 infection may not confer immunity against a different SARS-CoV-2 strain.

Automated detection algorithm for C4d immunostaining showed comparable diagnostic performance to pathologists in renal allograft biopsy.

A deep learning-based image analysis could improve diagnostic accuracy and efficiency in pathology work. Recently, we proposed a deep learning-based detection algorithm for C4d immunostaining in renal allografts. The objective of this study is to assess the diagnostic performance of the algorithm by comparing pathologists' diagnoses and analyzing the associations of the algorithm with clinical data. C4d immunostaining slides of renal allografts were obtained from two different institutions (100 slides from the Asan Medical Center and 86 slides from the Seoul National University Hospital) and scanned using two different slide scanners. Three pathologists and the algorithm independently evaluated each slide according to the Banff 2017 criteria. Subsequently, they jointly reviewed the results for consensus scoring. The result of the algorithm was compared with that of each pathologist and the consensus diagnosis. Clinicopathological associations of the results of the algorithm with allograft survival, histologic evidence of microvascular inflammation, and serologic results for donor-specific antibodies were also analyzed. As a result, the reproducibility between the pathologists was fair to moderate (kappa 0.36-0.54), which is comparable to that between the algorithm and each pathologist (kappa 0.34-0.51). The C4d scores predicted by the algorithm achieved substantial concordance with the consensus diagnosis (kappa = 0.61), and they were significantly associated with remarkable microvascular inflammation (P = 0.001), higher detection rate of donor-specific antibody (P = 0.003), and shorter graft survival (P < 0.001). In conclusion, the deep learning-based C4d detection algorithm showed a diagnostic performance similar to that of the pathologists.

Optimal matrix size of chest radiographs for computer-aided detection on lung nodule or mass with deep learning.

OBJECTIVES: To investigate the optimal input matrix size for deep learning-based computer-aided detection (CAD) of nodules and masses on chest radiographs. METHODS: We retrospectively collected 2088 abnormal (nodule/mass) and 352 normal chest radiographs from two institutions. Three thoracic radiologists drew 2758 abnormalities regions. A total of 1736 abnormal chest radiographs were used for training and tuning convolutional neural networks (CNNs). The remaining 352 abnormal and 352 normal chest radiographs were used as a test set. Two CNNs (Mask R-CNN and RetinaNet) were selected to validate the effects of the squared different matrix size of chest radiograph (256, 448, 896, 1344, and 1792). For comparison, figure of merit (FOM) of jackknife free-response receiver operating curve and sensitivity were obtained. RESULTS: In Mask R-CNN, matrix size 896 and 1344 achieved significantly higher FOM (0.869 and 0.856, respectively) for detecting abnormalities than 256, 448, and 1792 (0.667-0.820) (p < 0.05). In RetinaNet, matrix size 896 was significantly higher FOM (0.906) than others (0.329-0.832) (p < 0.05). For sensitivity of abnormalities, there was a tendency to increase sensitivity when lesion size increases. For small nodules (< 10 mm), the sensitivities were 0.418 and 0.409, whereas the sensitivities were 0.937 and 0.956 for masses. Matrix size 896 and 1344 in Mask R-CNN and matrix size 896 in RetinaNet showed significantly higher sensitivity than others (p < 0.05). CONCLUSIONS: Matrix size 896 had the highest performance for various sizes of abnormalities using different CNNs. The optimal matrix size of chest radiograph could improve CAD performance without additional training data. KEY POINTS: • Input matrix size significantly affected the performance of a deep learning-based CAD for detection of nodules or masses on chest radiographs. • The matrix size 896 showed the best performance in two different CNN detection models. • The optimal matrix size of chest radiographs could enhance CAD performance without additional training data.

Characteristics of Vanadium Oxide Thin Films Fabricated by Unbalanced Magnetron Sputtering for Smart Window Application.

Vanadium oxide (VOx) thin films were deposited by an unbalanced magnetron (UBM) sputtering system with a vanadium metal target and O2 reaction gas, and thermally treated at various annealing temperatures. In this work, the structural, electrical, and optical properties of the fabricated VOx films with various annealing temperatures were experimentally investigated. The UBM sputter grown VOx thin films exhibited amorphous structure, and had a very weak peak of V2O5 (002) owing to very thin films. However, the crystallite size of VOx films increased with increasing annealing temperature. The surface roughness of VOx films and average transmittance decreased with increasing annealing temperature. The resistivity of VOx films also decreased with increasing annealing temperature, while the electrical properties of films improved.

Comparison of the effects of peramivir and oseltamivir on the rise in platelet count in patients with or without proven influenza
.

OBJECTIVE: Neuraminidase (sialidase) inhibitors are considered to delay platelet clearance through the inhibition of platelet desialylation. A novel neuraminidase inhibitor, peramivir, was recently approved for intravenous administration by the US FDA. We aimed to compare the effects of peramivir and oseltamivir on patient platelet count. MATERIALS AND METHODS: Consecutive patients who were treated with peramivir or tested positive for influenza between January 2015 and December 2017 were analyzed. The analysis included 461 patients with platelet counts available; the patients were divided into three groups: patients with proven influenza treated with peramivir (n = 305); those treated with peramivir without proven influenza (n = 83), and those with proven influenza treated with oseltamivir (n = 73). RESULTS: Patients treated with peramivir did not show an increase in platelet count from the baseline count, regardless of proven influenza (from 263.4 × 109/L to 267.4 × 109/L; 9 = 0.410) or not (from 257.1 × 109/L to 255.4 × 109/L; p = 0.873); wheeras for patients treated with oseltamivir, a significant increase above the baseline was found (from 223.3 × 109/L to 249.9 × 109/L; p = 0.016), although it was transient. CONCLUSION: Peramivir and oseltamivir appear to have different effects on patient platelet count when administered at the recommended doses.
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Redistribution of Welding Residual Stresses of Crack Tip Opening Displacement Specimen by Local Compression.

The demand of crack tip opening displacement (CTOD) test which evaluates fracture toughness of a cracked material is very important to ensure the stability of structure under severe service environment. The validity of the CTOD test result is judged using several criterions of the specification standards. One of them is the artificially generated fatigue pre-crack length inside the specimen. For acceptable CTOD test results, fatigue pre-crack must have a reasonable sharp crack front. The propagation of fatigue crack started from the tip of the machined notch, which might have propagated irregularly due to residual stress field. To overcome this problem, test codes suggest local compression method, reversed bending method and stepwise high-R ratio method to reduce the disparity of residual stress distribution inside the specimen. In this paper, the relation between the degree of local compression and distribution of welding residual stress has been analyzed by finite element analyses in order to determine the amount of effective local compression of the test piece. Analysis results show that initial welding residual stress is dramatically varied three-dimensionally while cutting, notch machining and local compressing due to the change of internal restraint force. From the simulation result, the authors find that there is an optimum amount of local compression to modify regularly for generating fatigue pre-crack propagation. In the case of 0.5% compressions of the model width is the most effective for uniforming residual stress distribution.

Comparison between Saliva and Nasopharyngeal Swab Specimens for Detection of Respiratory Viruses by Multiplex Reverse Transcription-PCR.

Nasopharyngeal swabs (NPSs) are being widely used as specimens for multiplex real-time reverse transcription (RT)-PCR for respiratory virus detection. However, it remains unclear whether NPS specimens are optimal for all viruses targeted by multiplex RT-PCR. In addition, the procedure to obtain NPS specimens causes coughing in most patients, which possibly increases the risk of nosocomial spread of viruses. In this study, paired NPS and saliva specimens were collected from 236 adult male patients with suspected acute respiratory illnesses. Specimens were tested for 16 respiratory viruses by multiplex real-time RT-PCR. Among the specimens collected from the 236 patients, at least 1 respiratory virus was detected in 183 NPS specimens (77.5%) and 180 saliva specimens (76.3%). The rates of detection of respiratory viruses were comparable for NPS and saliva specimens (P = 0.766). Nine virus species and 349 viruses were isolated, 256 from NPS specimens and 273 from saliva specimens (P = 0.1574). Adenovirus was detected more frequently in saliva samples (P < 0.0001), whereas influenza virus type A and human rhinovirus were detected more frequently in NPS specimens (P = 0.0001 and P = 0.0289, respectively). The possibility of false-positive adenovirus detection from saliva samples was excluded by direct sequencing. In conclusion, neither of the sampling methods was consistently more sensitive than the other. We suggest that these cost-effective methods for detecting respiratory viruses in mixed NPS-saliva specimens might be valuable for future studies.

Effects of Korean Versus Western Diets on Reproductive Function in Young Korean Men: A 12-Week Randomized Parallel Clinical Trial.

Background: Studies report that diet may have contributed to a 50-60% decrease in human sperm quality over the past few decades. Unhealthy lifestyles affect the structure of spermatozoa, affecting the male reproductive potential. This study aimed to compare the effects of Korean and Western diets on reproductive function in young male Koreans. Methods: Study participants were provided either the Korean Diet (KD group) or the Western Diet (WD group) for 12 weeks. Semen quality parameters such as volume, motility, cell count, and sex hormone levels were evaluated. Sexual function was assessed using the International Index of Erectile Function and the Male Sexual Health Questionnaire. Efficacy and safety evaluations were conducted at baseline, 8 weeks, and 12 weeks. Results: The KD group demonstrated a significantly increased sperm motility after 8 weeks relative to baseline but decreased after 12 weeks. In contrast, sperm motility in the WD group significantly decreased after 8 weeks compared with baseline and remained constant after 12 weeks. Statistically, a near-significant difference was observed between groups (p = 0.057). Similarly, free testosterone levels in the KD group increased after 12 weeks compared with baseline, whereas that in the WD group decreased. The free testosterone levels in the KD group were significantly higher than those in the WD group (p = 0.020). There were no statistically significant differences in other sex hormone and sexual function questionnaires between the groups. None of the participants reported any severe side effects, and no significant alterations in clinical diagnostic test values were detected. Conclusion: The results of the study strongly reveal that KD positively affects sperm motility and male hormone levels in young men, indicating potential benefits for reproductive function.

Differential Diagnosis of Pleural Effusion Using Machine Learning.

Rationale: Differential diagnosis of pleural effusion is challenging in clinical practice. Objectives: We aimed to develop a machine learning model to classify the five common causes of pleural effusions. Methods: This retrospective study collected 49 features from clinical information, blood, and pleural fluid of adult patients who underwent diagnostic thoracentesis between October 2013 and December 2018. Pleural effusions were classified into the following five categories: transudative, malignant, parapneumonic, tuberculous, and other. The performance of five different classifiers, including multinomial logistic regression, support vector machine, random forest, extreme gradient boosting, and light gradient boosting machine (LGB), was evaluated in terms of accuracy and area under the receiver operating characteristic curve through fivefold cross-validation. Hybrid feature selection was applied to determine the most relevant features for classifying pleural effusion. Results: We analyzed 2,253 patients (training set, n = 1,459; validation set, n = 365; extra-validation set, n = 429) and found that the LGB model achieved the best performance in both validation and extra-validation sets. After feature selection, the accuracy of the LGB model with the selected 18 features was equivalent to that with all 49 features (mean ± standard deviation): 0.818 ± 0.012 and 0.777 ± 0.007 in the validation and extra-validation sets, respectively. The model's mean area under the receiver operating characteristic curve was as high as 0.930 ± 0.042 and 0.916 ± 0.044 in the validation and extra-validation sets, respectively. In our model, pleural lactate dehydrogenase, protein, and adenosine deaminase levels were the most important factors for classifying pleural effusions. Conclusions: Our LGB model showed satisfactory performance for differential diagnosis of the common causes of pleural effusions. This model could provide clinicians with valuable information regarding the major differential diagnoses of pleural diseases.