Which is better? Early versus delayed rehabilitation after arthroscopic rotator cuff repair.

PURPOSE: This study aimed to compare the 5-year clinical and functional outcomes, including repaired tendon healing status, between early and delayed rehabilitation after arthroscopic rotator cuff repair METHODS: A total of 75 patients with rotator cuff tears (less than 5 cm) underwent arthroscopic repairs over a 60-month period. Participants were randomly assigned to early and delayed postoperative rehabilitation groups with distinct protocols. Clinical and functional outcome measures included Constant score, University of California at Los Angeles (UCLA) score, visual analogue scale for pain and isokinetic dynamometer test for muscle strength recovery. Clinical and functional scores were compared between baseline and 5 years postoperatively. Radiologic assessment via magnetic resonance imaging was performed at a minimum of 12 months postoperatively for evaluations of tendon integrity and recurrent tears. RESULTS: Baseline characteristics showed no statistically significant differences between groups. Both groups demonstrated equivalent improvement in range of motion and pain scores with no statistical differences. Clinical scores improved significantly in both groups by postoperative 12 months and plateaued. At the postoperative 5-year mark, the early group showed better improvement in the visual analogue scale and UCLA score, while the delayed group had superior Constant scores. Postoperative magnetic resonance imaging revealed six recurrent tears, two in the early group and four in the delayed group, with no statistical differences. Muscle strength recovery showed no differences between the two groups. CONCLUSION: Both the early and the delayed rehabilitation groups showed similar outcomes in postoperative range of motion, functional scores, muscle strength recovery and tendon healing in the short- and mid-term follow-ups. LEVEL OF EVIDENCE: Level III.

Deep Learning for Estimating Lung Capacity on Chest Radiographs Predicts Survival in Idiopathic Pulmonary Fibrosis.

Background Total lung capacity (TLC) has been estimated with use of chest radiographs based on time-consuming methods, such as planimetric techniques and manual measurements. Purpose To develop a deep learning-based, multidimensional model capable of estimating TLC from chest radiographs and demographic variables and validate its technical performance and clinical utility with use of multicenter retrospective data sets. Materials and Methods A deep learning model was pretrained with use of 50 000 consecutive chest CT scans performed between January 2015 and June 2017. The model was fine-tuned on 3523 pairs of posteroanterior chest radiographs and plethysmographic TLC measurements from consecutive patients who underwent pulmonary function testing on the same day. The model was tested with multicenter retrospective data sets from two tertiary care centers and one community hospital, including (a) an external test set 1 (n = 207) and external test set 2 (n = 216) for technical performance and (b) patients with idiopathic pulmonary fibrosis (n = 217) for clinical utility. Technical performance was evaluated with use of various agreement measures, and clinical utility was assessed in terms of the prognostic value for overall survival with use of multivariable Cox regression. Results The mean absolute difference and within-subject SD between observed and estimated TLC were 0.69 L and 0.73 L, respectively, in the external test set 1 (161 men; median age, 70 years [IQR: 61-76 years]) and 0.52 L and 0.53 L in the external test set 2 (113 men; median age, 63 years [IQR: 51-70 years]). In patients with idiopathic pulmonary fibrosis (145 men; median age, 67 years [IQR: 61-73 years]), greater estimated TLC percentage was associated with lower mortality risk (adjusted hazard ratio, 0.97 per percent; 95% CI: 0.95, 0.98; P < .001). Conclusion A fully automatic, deep learning-based model estimated total lung capacity from chest radiographs, and the model predicted survival in idiopathic pulmonary fibrosis. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Sorkness in this issue.

Can multiple miniplates improve the treatment of comminuted patellar fracture?

BACKGROUND: We have applied primarily multiple locking compression miniplates in treating multifragmentary, comminuted patellar fracture in combination with conventional fixation methods. METHODS: Medical and radiologic data were retrospectively reviewed for the patients surgically fixated with locking compression miniplates in patellar fracture of AO/OTA 34-C3. The primary outcome was bone union at the final follow-up, and the secondary outcomes were functional outcomes and postoperative complications associated with the procedure. For the functional assessment, the Lysholm score, Tegner scores, and the knee range of motion was compared. RESULTS: A total of twenty patients with AO/OTA 34-C3 patellar fracture were included in the study with an average follow-up period of 15 months (range:11 ~ 18 months) between June 2018 and November 2021. Eleven male and nine female patients presented an average age of 57.15 years. The primary fracture union was seen in all twenty patients, and the average time to the union was 15.6 weeks on serial radiograph follow-up. All patients did not show any postoperative complications, such as fixation failure, infection, or revision operations. Postoperatively, all patients achieved an average range of motion of 130 degrees, and the Lysholm and Tegners scores showed an average of 90.4 and 5.0 at the final follow-up, retrospectively. CONCLUSION: Fixations with miniplates in comminuted patellar fractures can be a useful option for effective osteosynthesis due to their versatile, efficient, and low-profile nature.

The first case report of thorn-induced Alternaria alternata infection of the hand in an immunocompetent host.

BACKGROUND: Fungal infection secondary to a penetrating plant thorn injury in upper extremities is infrequently reported especially in immunocompetent hosts. Alternaria is a dematicaceous hyphomycete, commonly found in decay and plant pathogens, and Alternaria alternata has been regarded as the most frequent species among more than 400 s of its species. This case is the first report of thorn-induced Alternaria alternata infection of the hand in an immunocompetent host. CASE PRESENTATION: A 47-year-old male patient was admitted to our institution with persistent pain and swelling of the right hand even after a prior surgical removal of a previous thorn injury. Upon impression of abscess, chronic extensor tenosynovitis, and septic arthritis at the 3rd metacarpophalangeal joint based on advanced imaging, the patient underwent surgical incision and drainage. Intraoperative culture, biopsy, and gene molecular sequencing results revealed fungal infection with Alternaria alternata. Postoperatively, the patient was treated with oral itraconazole (200 mg q 12 h) for nine consecutive months. CONCLUSIONS: We report the first case of chronic extensor tenosynovitis and septic arthritis of the hand with Alternaria alternata after a thorn injury in an immunocompetent host. Despite rare incidences of fungal extensor tenosynovitis and septic arthritis, the current case strongly suggests a careful examination of social history and surgical debridement along with a prolonged use of appropriate anti-fungal agents in thorn injuries.

Deep Learning-Based Automatic CT Quantification of Coronavirus Disease 2019 Pneumonia: An International Collaborative Study.

OBJECTIVE: We aimed to develop and validate the automatic quantification of coronavirus disease 2019 (COVID-19) pneumonia on computed tomography (CT) images. METHODS: This retrospective study included 176 chest CT scans of 131 COVID-19 patients from 14 Korean and Chinese institutions from January 23 to March 15, 2020. Two experienced radiologists semiautomatically drew pneumonia masks on CT images to develop the 2D U-Net for segmenting pneumonia. External validation was performed using Japanese (n = 101), Italian (n = 99), Radiopaedia (n = 9), and Chinese data sets (n = 10). The primary measures for the system's performance were correlation coefficients for extent (%) and weight (g) of pneumonia in comparison with visual CT scores or human-derived segmentation. Multivariable logistic regression analyses were performed to evaluate the association of the extent and weight with symptoms in the Japanese data set and composite outcome (respiratory failure and death) in the Spanish data set (n = 115). RESULTS: In the internal test data set, the intraclass correlation coefficients between U-Net outputs and references for the extent and weight were 0.990 and 0.993. In the Japanese data set, the Pearson correlation coefficients between U-Net outputs and visual CT scores were 0.908 and 0.899. In the other external data sets, intraclass correlation coefficients were between 0.949-0.965 (extent) and between 0.978-0.993 (weight). Extent and weight in the top quartile were independently associated with symptoms (odds ratio, 5.523 and 10.561; P = 0.041 and 0.016) and the composite outcome (odds ratio, 9.365 and 7.085; P = 0.021 and P = 0.035). CONCLUSIONS: Automatically quantified CT extent and weight of COVID-19 pneumonia were well correlated with human-derived references and independently associated with symptoms and prognosis in multinational external data sets.

Automatic pulmonary vessel segmentation on noncontrast chest CT: deep learning algorithm developed using spatiotemporally matched virtual noncontrast images and low-keV contrast-enhanced vessel maps.

OBJECTIVES: To develop a deep learning-based pulmonary vessel segmentation algorithm (DLVS) from noncontrast chest CT and to investigate its clinical implications in assessing vascular remodeling of chronic obstructive lung disease (COPD) patients. METHODS: For development, 104 pulmonary CT angiography scans (49,054 slices) using a dual-source CT were collected, and spatiotemporally matched virtual noncontrast and 50-keV images were generated. Vessel maps were extracted from the 50-keV images. The 3-dimensional U-Net-based DLVS was trained to segment pulmonary vessels (with a vessel map as the output) from virtual noncontrast images (as the input). For external validation, vendor-independent noncontrast CT images (n = 14) and the VESSEL 12 challenge open dataset (n = 3) were used. For each case, 200 points were selected including 20 intra-lesional points, and the probability value for each point was extracted. For clinical validation, we included 281 COPD patients with low-dose noncontrast CTs. The DLVS-calculated volume of vessels with a cross-sectional area < 5 mm2 (PVV5) and the PVV5 divided by total vessel volume (%PVV5) were measured. RESULTS: DLVS correctly segmented 99.1% of the intravascular points (1,387/1,400) and 93.1% of the extravascular points (1,309/1,400). The areas-under-the receiver-operating characteristic curve (AUROCs) were 0.977 and 0.969 for the two external validation datasets. For the COPD patients, both PPV5 and %PPV5 successfully differentiated severe patients whose FEV1 < 50 (AUROCs; 0.715 and 0.804) and were significantly correlated with the emphysema index (Ps < .05). CONCLUSIONS: DLVS successfully segmented pulmonary vessels on noncontrast chest CT by utilizing spatiotemporally matched 50-keV images from a dual-source CT scanner and showed promising clinical applicability in COPD. KEY POINTS: • We developed a deep learning pulmonary vessel segmentation algorithm using virtual noncontrast images and 50-keV enhanced images produced by a dual-source CT scanner. • Our algorithm successfully segmented vessels on diseased lungs. • Our algorithm showed promising results in assessing the loss of small vessel density in COPD patients.

Automatic prediction of left cardiac chamber enlargement from chest radiographs using convolutional neural network.

OBJECTIVE: To develop deep learning-based cardiac chamber enlargement-detection algorithms for left atrial (DLCE-LAE) and ventricular enlargement (DLCE-LVE), on chest radiographs METHODS: For training and internal validation of DLCE-LAE and -LVE, 5,045 chest radiographs (CRs; 2,463 normal and 2,393 LAE) and 1,012 CRs (456 normal and 456 LVE) matched with the same-day echocardiography were collected, respectively. External validation was performed using 107 temporally independent CRs. Reader performance test was conducted using the external validation dataset by five cardiothoracic radiologists without and with the results of DLCE. Classification performance of DLCE was evaluated and compared with those of the readers and conventional radiographic features, including cardiothoracic ratio, carinal angle, and double contour. In addition, DLCE-LAE was tested on 5,277 CRs from a healthcare screening program cohort. RESULTS: DLCE-LAE showed areas under the receiver operating characteristics curve (AUROCs) of 0.858 on external validation. On reader performance test, DLCE-LAE showed better results than pooled radiologists (AUROC 0.858 vs. 0.651; p < .001) and significantly increased their performance when used as a second reader (AUROC 0.651 vs. 0.722; p < .001). DLCE-LAE also showed a significantly higher AUROC than conventional radiographic findings (AUROC 0.858 vs. 0.535-0.706; all ps < .01). In the healthcare screening cohort, DLCE-LAE successfully detected 71.0% (142/200) CRs with moderate-to-severe LAE (93.5% [29/31] of severe cases), while yielding 11.8% (492/4,184) false-positive rate. DLCE-LVE showed AUROCs of 0.966 and 0.594 on internal and external validation, respectively. CONCLUSION: DLCE-LAE outperformed and improved cardiothoracic radiologists' performance in detecting LAE and showed promise in screening individuals with moderate-to-severe LAE in a healthcare screening cohort. KEY POINTS: • Our deep learning algorithm outperformed cardiothoracic radiologists in detecting left atrial enlargement on chest radiographs. • Cardiothoracic radiologists improved their performance in detecting left atrial enlargement when aided by the algorithm. • On a healthcare-screening cohort, our algorithm detected 71.0% (142/200) radiographs with moderate-to-severe left atrial enlargement while yielding 11.8% (492/4,184) false-positive rate.

Deep learning algorithm for surveillance of pneumothorax after lung biopsy: a multicenter diagnostic cohort study.

OBJECTIVES: Pneumothorax is the most common and potentially life-threatening complication arising from percutaneous lung biopsy. We evaluated the performance of a deep learning algorithm for detection of post-biopsy pneumothorax in chest radiographs (CRs), in consecutive cohorts reflecting actual clinical situation. METHODS: We retrospectively included post-biopsy CRs of 1757 consecutive patients (1055 men, 702 women; mean age of 65.1 years) undergoing percutaneous lung biopsies from three institutions. A commercially available deep learning algorithm analyzed each CR to identify pneumothorax. We compared the performance of the algorithm with that of radiology reports made in the actual clinical practice. We also conducted a reader study, in which the performance of the algorithm was compared with those of four radiologists. Performances of the algorithm and radiologists were evaluated by area under receiver operating characteristic curves (AUROCs), sensitivity, and specificity, with reference standards defined by thoracic radiologists. RESULTS: Pneumothorax occurred in 17.5% (308/1757) of cases, out of which 16.6% (51/308) required catheter drainage. The AUROC, sensitivity, and specificity of the algorithm were 0.937, 70.5%, and 97.7%, respectively, for identification of pneumothorax. The algorithm exhibited higher sensitivity (70.2% vs. 55.5%, p < 0.001) and lower specificity (97.7% vs. 99.8%, p < 0.001), compared with those of radiology reports. In the reader study, the algorithm exhibited lower sensitivity (77.3% vs. 81.8-97.7%) and higher specificity (97.6% vs. 81.7-96.0%) than the radiologists. CONCLUSION: The deep learning algorithm appropriately identified pneumothorax in post-biopsy CRs in consecutive diagnostic cohorts. It may assist in accurate and timely diagnosis of post-biopsy pneumothorax in clinical practice. KEY POINTS: • A deep learning algorithm can identify chest radiographs with post-biopsy pneumothorax in multicenter consecutive cohorts reflecting actual clinical situation. • The deep learning algorithm has a potential role as a surveillance tool for accurate and timely diagnosis of post-biopsy pneumothorax.

Associations between urinary cotinine and symptoms of attention deficit/hyperactivity disorder and autism spectrum disorder.

BACKGROUND: The present study investigated associations between urinary cotinine levels as a biomarker of secondhand smoke exposure and symptoms of attention deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). METHODS: A total of 520 child participants (200 with ADHD, 67 with ASD, and 253 normal control subjects) were assessed using the Korean version of the ADHD rating scale (K-ARS), Autism spectrum screening questionnaire (ASSQ), and Behavioral Assessment System for Children, second edition (BASC-2). The Korean version of the computer-based continuous performance test was used to assess cognitive function. Urinary cotinine was evaluated as a biomarker of secondhand smoke exposure. RESULTS: Urinary cotinine levels were significantly and positively associated with K-ARS score (B = 4.00, p < 0.001), ASSQ score (B = 1.71, p = 0.030), the behavioral problem subscales of the BASC-2 (B = 1.68-3.52, p < 0.001-0.045), and omission and commission errors in the continuous performance test (B = 6.21-8.42, p < 0.001-0.019). Urinary cotinine levels were also associated with the increased odds ratio of ADHD (OR = 1.55, 95% CI 1.05-2.30, p = 0.028) and ASD (OR = 1.89, 95% CI 1.12-3.21, p = 0.018). CONCLUSION: Urinary cotinine levels were associated with lower behavioral adaptation and cognitive function and increased odds ratios of ADHD and ASD, indicating a negative effect of secondhand smoke exposure on the symptomatic manifestation of ADHD and ASD.

Associations between attention-deficit/hyperactivity disorder symptoms and dietary habits in elementary school children.

OBJECTIVE: The aim of the present study was to investigate the associations between dietary habits and attention deficit/hyperactivity disorder (ADHD) symptoms in elementary school children. METHODS: The parents of 16,831 participating children assessed the ADHD symptoms of their children by responding to the Korean version of the ADHD rating scale (K-ARS). Parents also responded to the food habit questionnaire, which consists of 8 items regarding the eating pace, the frequency of overeating, and patterns of eating six types of food: fast food, soft drinks, instant noodles, fruit and vegetables, and milk. RESULTS: K-ARS scores were positively associated with higher consumption of foods categorized as unhealthy, including fast food, soft drinks, and instant noodles, and negatively associated with higher consumption of fruit and vegetables categorized as healthy foods. K-ARS scores were also higher in the groups who overate more frequently and ate faster or slower compared to other family members. CONCLUSION: Our findings may provide useful clinical information for dietary interventions in children with ADHD.

Secondhand Smoke Exposure and Low Blood Lead Levels in Association With Attention-Deficit Hyperactivity Disorder and Its Symptom Domain in Children: A Community-Based Case-Control Study.

AIM: Secondhand smoke (SHS) is a major indoor pollutant. We examined the possible association between exposure to both SHS and low levels of lead and attention-deficit-hyperactivity disorder (ADHD) and its symptom domain in children. METHODS: This case-control study was based on the results of a community survey using the ADHD rating scale conducted in 49 elementary schools. Both cases and control subjects were confirmed by a child psychiatrist. Each case was matched with one control subject according to gender, school, and grade in school. Using a multivariate conditional logistic regression model, we analyzed 214 case-control pairs of children who ranged in age from 6 to 10 years. Urine and blood levels of cotinine and of lead were determined, and information pertaining to SHS exposure was obtained by means of a questionnaire. RESULTS: Exposure to low levels of lead (geometric mean = 1.65 µg/dL) was related to ADHD, particularly inattention (odds ratio [OR] = 1.67, 95% confidence interval [CI] = 1.07-2.59), whereas SHS exposure was associated mainly with hyperactivity/impulsivity (OR = 3.85, 95% CI = 1.55-9.56). In the pathway from blood lead to hyperactivity/impulsivity, children's SHS exposure mediated and indirectly accounted for about 73% of this relationship. The combined exposure to lead and SHS synergistically increased the risk of ADHD, evident as both inattention and hyperactivity/impulsivity. CONCLUSION: SHS, which is associated with hyperactivity/impulsivity in particular, combined with exposure to low blood levels of lead synergistically increased the risk of ADHD. Therefore, the exposure of children to both SHS and lead needs to be reduced. IMPLICATIONS: Although exposure to low levels of lead has been shown to be associated with ADHD, there is little evidence of symptom domain specificity. In our study, low blood lead levels were related to inattention. In addition, prenatal or postnatal exposure to SHS increased the risk of ADHD, particularly hyperactivity/impulsivity. Combined exposure to lead and SHS synergistically increased the risk for both these ADHD symptom domains. To protect children from environmental risk factors related to ADHD, it is necessary to further reduce children's exposure to SHS and lead, even in those with low blood lead levels.

Prevalence of Attention-Deficit/Hyperactivity Disorder and its Comorbidity among Korean Children in a Community Population.

The aim of this study was to determine the prevalence of attention deficit hyperactivity disorders (ADHD) in children according to socio-demographic factors and the distribution of ADHD subtypes in a community in Korea. A screening survey using the Korean version of ADHD Rating Scale (K-ARS) was conducted between 2007 and 2008, and clinical interviews by a pediatric psychiatrist were performed for selected children between 2009 and 2010. A total of 49,573 elementary school students, between ages of 7 and 12, constituted the target population, among which 38,365 students (77.2%) and respective parents gave consent to participate. Of the participants, 200 screened children were clinically examined to confirm the diagnosis of ADHD. We estimated the prevalence of ADHD and its comorbidity in the population, after adjusting for nonresponse and nonparticipation. The prevalence of ADHD was 11.7% in boys and 5.2% in girls, with an overall prevalence of 8.5%. The combined type of inattentive and hyperactive was the most frequent at 4.7% of the whole population. Children were more likely to have ADHD if their parents were separated and had less education. Most commonly combined comorbidity was autism spectrum disorder (ASD) (10.1%). The prevalence of ADHD in the school-aged population is an essential information for improving the quality of public health mental services for evaluation and treatment of ADHD.

Three cases of postoperative septic arthritis caused by Mycobacterium conceptionense in the shoulder joints of immunocompetent patients.

Mycobacterium conceptionense is a species member of Mycobacterium fortuitum complex, a potential pathogen of increasing clinical importance among opportunistic infections. This species causes a wide spectrum of cutaneous and extracutaneous diseases. In this report, we describe three patients who underwent shoulder surgery with postoperative joint infection by M. conceptionense.

Reactive oxygen species in signalling the transcriptional activation of WIPK expression in tobacco.

Plant mitogen-activated protein kinases represented by tobacco WIPK (wounding-induced protein kinase) and its orthologs in other species are unique in their regulation at transcriptional level in response to stress and pathogen infection. We previously demonstrated that transcriptional activation of WIPK is essential for induced WIPK activity, and activation of salicylic acid-induced protein kinase (SIPK) by the constitutively active NtMEK2(DD) is sufficient to induce WIPK gene expression. Here, we report that the effect of SIPK on WIPK gene expression is mediated by reactive oxygen species (ROS). Using a combination of pharmacological and gain-of-function transgenic approaches, we studied the relationship among SIPK activation, WIPK gene activation in response to fungal cryptogein, light-dependent ROS generation in chloroplasts, and ROS generated via NADPH oxidase. In the conditional gain-of-function GVG-NtMEK2(DD) transgenic tobacco, induction of WIPK expression is dependent on the ROS generation in chloroplasts. Consistently, methyl viologen, an inducer of ROS generation in chloroplasts, highly activated WIPK expression. In addition to chloroplast-originated ROS, H(2)O(2) generated from the cell-surface NADPH oxidase could also activate WIPK gene expression, and inhibition of cryptogein-induced ROS generation also abolished WIPK gene activation. Our data demonstrate that WIPK gene activation is mediated by ROS, which provides a mechanism by which ROS influence cellular signalling processes in plant stress/defence response.

The associations between ADHD and asthma in Korean children.

BACKGROUND: The aim of this study was to investigate the associations between attention deficit hyperactivity disorder (ADHD), the most common neuropsychiatric disorder in school children, and childhood allergic disease by evaluating their respective prevalence. METHODS: Subjects were comprised of first and second grade students in twenty two elementary schools in a city in the Republic of Korea. The mode of measurement for ADHD was based on DSM-IV from clinical interviews conducted by child psychiatrists. Along with the diagnostic interviews, we also used the epidemiological questionnaires, Computerized Attention Deficit-Hyperactivity Disorder Diagnostic System, the abbreviated Conner's Parent Rating Scale (CPRS), and DuPaul's ADHD Rating Scales. Allergic conditions, such as asthma, have been separately evaluated based on the questionnaire items whose validity and reliability were proved by the International Study of Asthma and Allergies in Children (ISAAC). All questionnaires were completed by the subjects' parents. RESULTS: The lifetime prevalence rate of asthma in ADHD patients was 36.6%, compared to a prevalence of 24.3% in control subjects. The lifetime prevalence rate of allergic rhinitis in ADHD patients was 59.0%, compared to a prevalence of 47.0% in control subjects. Statistically significant difference has been found between the two groups. In the logistic regression model of the ADHD and the control group, the relative risk of asthma was 1.60 times higher (confidence interval 1.301-1.964), the relative risk of allergic rhinitis was 1.38 times higher (confidence interval 1.124-1.681), which showed statistical significance. CONCLUSIONS: The findings of this study suggest significant association between ADHD and childhood asthma and allergic rhinitis. Therefore, appropriate evaluation and treatment are needed for asthmatic children with attention-deficit symptoms, or allergic rhinitis with ADHD. Besides, further research is needed to determine the etiological approach towards ADHD, asthma, and allergic rhinitis.

Prospective evaluation of deep learning image reconstruction for Lung-RADS and automatic nodule volumetry on ultralow-dose chest CT.

PURPOSE: To prospectively evaluate whether Lung-RADS classification and volumetric nodule assessment were feasible with ultralow-dose (ULD) chest CT scans with deep learning image reconstruction (DLIR). METHODS: The institutional review board approved this prospective study. This study included 40 patients (mean age, 66±12 years; 21 women). Participants sequentially underwent LDCT and ULDCT (CTDIvol, 0.96±0.15 mGy and 0.12±0.01 mGy) scans reconstructed with the adaptive statistical iterative reconstruction-V 50% (ASIR-V50) and DLIR. CT image quality was compared subjectively and objectively. The pulmonary nodules were assessed visually by two readers using the Lung-RADS 1.1 and automatically using a computerized assisted tool. RESULTS: DLIR provided a significantly higher signal-to-noise ratio for LDCT and ULDCT images than ASIR-V50 (all P < .001). In general, DLIR showed superior subjective image quality for ULDCT images (P < .001) and comparable quality for LDCT images compared to ASIR-V50 (P = .01-1). The per-nodule sensitivities of observers for Lung-RADS category 3-4 nodules were 70.6-88.2% and 64.7-82.4% for DLIR-LDCT and DLIR-ULDCT images (P = 1) and categories were mostly concordant within observers. The per-nodule sensitivities of the computer-assisted detection for nodules ≥4 mm were 72.1% and 67.4% on DLIR-LDCT and ULDCT images (P = .50). The 95% limits of agreement for nodule volume differences between DLIR-LDCT and ULDCT images (-85.6 to 78.7 mm3) was similar to the within-scan nodule volume differences between DLIR- and ASIR-V50-LDCT images (-63.9 to 78.5 mm3), with volume differences smaller than 25% in 88.5% and 92.3% of nodules, respectively (P = .65). CONCLUSION: DLIR enabled comparable Lung-RADS and volumetric nodule assessments on ULDCT images to LDCT images.

Risk factors of mortality in patients with rheumatoid arthritis-associated interstitial lung disease: a single-centre prospective cohort study.

OBJECTIVES: To determine the risk factors for mortality in Korean patients with rheumatoid arthritis (RA)-associated interstitial lung disease (ILD) in comparison to patients with RA but without ILD (RA-nonILD). METHODS: Data were extracted from a single-centre prospective cohort of RA patients with a chest computed tomography scan at an academic referral hospital in Korea. Patients with RA-ILD enroled between May 2017 and August 2022 were selected, and those without ILD were selected as comparators. The mortality rate was calculated, and the causes of each death were investigated. We used Cox proportional hazard regression with Firth's penalised likelihood method to identify the risk factors for mortality in patients with RA-ILD. RESULTS: A total of 615 RA patients were included: 200 with ILD and 415 without ILD. In the RA-ILD group, there were 15 deaths over 540.1 person-years (PYs), resulting in mortality rate of 2.78/100 PYs. No deaths were reported in the RA-nonILD group during the 1669.9 PYs. The primary causes of death were infection (nine cases) and lung cancer (five cases), with only one death attributed to ILD aggravation. High RA activity (adjusted HR 1.87, CI 1.16-3.10), baseline diffusing capacity for carbon monoxide (DLCO) < 60% (adjusted HR 4.88, 95% CI 1.11-45.94), and usual interstitial pneumonia (UIP) pattern (adjusted HR 5.13, 95% CI 1.00-57.36) were identified as risk factors for mortality in RA-ILD patients. CONCLUSION: Patients with RA-ILD have an elevated risk of mortality compared with those without ILD. Infection-related deaths are the main causes of mortality in this population. High RA activity, low DLCO, and the UIP pattern are significantly associated with the mortality in patients with RA-ILD.

Clinical significance of the advanced lung cancer inflammation index in patients with limited-stage small cell lung cancer treated with chemoradiotherapy.

The aim of the study was to investigate the prognostic significance of the advanced lung cancer inflammation index (ALI) in patients with limited-stage small-cell lung cancer (LS-SCLC) undergoing definite chemo-radiotherapy (CRT). We included 87 patients with LS-SCLC from South Korea, treated between 2005 and 2019 with definite CRT. ALI was calculated using body mass index, serum albumin, and neutrophil-lymphocyte ratio. We categorized 38 patients into the high ALI group (ALI ≥ 44.3) and 48 into the low ALI group (ALI < 44.3). Patients in the high ALI group exhibited longer overall survival (OS) than patients in the low ALI group. In multivariate analysis, prophylactic cranial irradiation (hazard ratio [HR] = 0.366, 95% confidence interval [CI] 0.20-0.66, P = 0.0008), and high ALI (HR = 0.475, 95% CI 0.27-0.84, P = 0.0103) were identified as independent prognostic factors for predicting better OS. Notably, a high ALI score was particularly indicative of longer survival in patients treated with the combination of etoposide and cisplatin. In conclusion, this study demonstrated that a high pretreatment ALI was significantly associated with better OS in patients with LS-SCLC undergoing definite CRT. This suggests that ALI could be a useful tool for predicting prognosis and guiding chemotherapy regimen selections in clinical practice for LS-SCLC.

Putrescine regulating by stress-responsive MAPK cascade contributes to bacterial pathogen defense in Arabidopsis.

Polyamines in plants are involved in various physiological and developmental processes including abiotic and biotic stress responses. We investigated the expression of ADCs, which are key enzymes in putrescine (Put) biosynthesis, and roles of Put involving defense response in Arabidopsis. The increased expression of ADC1 and ADC2, and the induction of Put were detected in GVG-NtMEK2(DD) transgenic Arabidopsis, whereas, their performance was partially compromised in GVG-NtMEK2(DD)/mpk3 and GVG-NtMEK2(DD)/mpk6 mutant following DEX treatment. The expression of ADC2 was highly induced by Pst DC3000 inoculation, while the transcript levels of ADC1 were slightly up-regulated. Compared to the WT plant, Put content in the adc2 knock-out mutant was reduced after Pst DC3000 inoculation, and showed enhanced susceptibility to pathogen infection. The adc2 mutant exhibited reduced expression of PR-1 after bacterial infection and the growth of the pathogen was about 4-fold more than that in the WT plant. Furthermore, the disease susceptibility of the adc2 mutant was recovered by the addition of exogenous Put. Taken together, these results suggest that Arabidopsis MPK3 and MPK6 play a positive role in the regulation of Put biosynthesis, and that Put contributes to bacterial pathogen defense in Arabidopsis.

Synthesis of poly(benzothiadiazole-co-dithienobenzodithiophenes) and effect of thiophene insertion for high-performance polymer solar cells.

We describe herein the synthesis of novel donor-acceptor conjugated polymers with dithienobenzodithiophenes (DTBDT) as the electron donor and 2,1,3-benzothiadiazole as the electron acceptor for high-performance organic photovoltaics (OPVs). We studied the effects of strategically inserting thiophene into the DTBDT as a substituent on the skeletal structure on the opto-electronic performances of fabricated devices. From UV/Vis absorption, electrochemical, and field-effect transistor analyses, we found that the thiophene-containing DTBDT derivative can substantially increase the orbital overlap area between adjacent conjugated chains and thus dramatically enhance charge-carrier mobility up to 0.55 cm(2) V(-1) s(-1). The outstanding charge-transport characteristics of this polymer allowed the realization of high-performance organic solar cells with a power conversion efficiency (PCE) of 5.1 %. Detailed studies on the morphological factors that enable the maximum PCE of the polymer solar cells are discussed along with a hole/electron mobility analysis based on the space-charge-limited current model.