The biological function of the type II toxin-antitoxin system ccdAB in recurrent urinary tract infections.

Urinary tract infections (UTIs) represent a significant challenge in clinical practice, with recurrent forms (rUTIs) posing a continual threat to patient health. Escherichia coli (E. coli) is the primary culprit in a vast majority of UTIs, both community-acquired and hospital-acquired, underscoring its clinical importance. Among different mediators of pathogenesis, toxin-antitoxin (TA) systems are emerging as the most prominent. The type II TA system, prevalent in prokaryotes, emerges as a critical player in stress response, biofilm formation, and cell dormancy. ccdAB, the first identified type II TA module, is renowned for maintaining plasmid stability. This paper aims to unravel the physiological role of the ccdAB in rUTIs caused by E. coli, delving into bacterial characteristics crucial for understanding and managing this disease. We investigated UPEC-induced rUTIs, examining changes in type II TA distribution and number, phylogenetic distribution, and Multi-Locus Sequence Typing (MLST) using polymerase chain reaction (PCR). Furthermore, our findings revealed that the induction of ccdB expression in E. coli BL21 (DE3) inhibited bacterial growth, observed that the expression of both ccdAB and ccdB in E. coli BL21 (DE3) led to an increase in biofilm formation, and confirmed that ccdAB plays a role in the development of persistent bacteria in urinary tract infections. Our findings could pave the way for novel therapeutic approaches targeting these systems, potentially reducing the prevalence of rUTIs. Through this investigation, we hope to contribute significantly to the global effort to combat the persistent challenge of rUTIs.

Evaluation of renal function in chronic kidney disease using histogram analysis based on multiple diffusion models.

OBJECTIVES: To compare the diagnostic value of histogram features of multiple diffusion metrics in predicting early renal impairment in chronic kidney disease (CKD). METHODS: A total of 77 patients with CKD (mild group, estimated glomerular filtration rate (eGFR) ≥60 mL/min/1.73 m2) and 30 healthy controls (HCs) were enrolled. Diffusion-weighted imaging was performed by using single-shot echo planar sequence with 13 b values (0, 20, 50, 80, 100, 150, 200, 500, 800, 1000, 1500, 2000, and 2500 s/mm2). Diffusion models including mono-exponential (Mono), intravoxel incoherent motion (IVIM), stretched-exponential (SEM), and kurtosis (DKI) were calculated, and their histogram features were analysed. All diffusion models for predicting early renal impairment in CKD were established using logistic regression analysis, and diagnostic efficiency was compared among the models. RESULTS: All diffusion models had high differential diagnosis efficiency between the mild group and HCs. The areas under the curve (AUCs) of Mono, IVIM, SEM, DKI, and the combined diffusion model for predicting early renal impairment in CKD were 0.829, 0.809, 0.760, 0.825, and 0.861, respectively. There were no significant differences in AUCs except SEM and combined model, SEM, and DKI model. There were significant correlations between eGFR/serum creatinine and some of histogram features. CONCLUSIONS: Histogram analysis based on multiple diffusion metrics was practicable for the non-invasive assessment of early renal impairment in CKD. ADVANCES IN KNOWLEDGE: Advanced diffusion models provided microstructural information. Histogram analysis further reflected histological characteristics and heterogeneity. Histogram analysis based on multiple diffusion models could provide an accurate and non-invasive method to evaluate the early renal damage of CKD.

Targeted elimination of Vancomycin resistance gene vanA by CRISPR-Cas9 system.

OBJECTIVE: The purpose of this study is to reduce the spread of the vanA gene by curing the vanA-harboring plasmid of vancomycin-resistant using the CRISPR-Cas9 system. METHODS: Two specific spacer sequence (sgRNAs) specific was designed to target the vanA gene and cloned into plasmid CRISPR-Cas9. The role of the CRISPR-Cas system in the plasmid elimination of drug-resistance genes was verified by chemically transformation and conjugation delivery methods. Moreover, the elimination efficiency in strains was evaluated by plate counting, PCR, and quantitative real-time PCR (qPCR). Susceptibility testing was performed by broth microdilution assay and by Etest strips (bioMérieux, France) to detect changes in bacterial drug resistance phenotype after drug resistance plasmid clearance. RESULTS: In the study, we constructed a specific prokaryotic CRISPR-Cas9 system plasmid targeting cleavage of the vanA gene. PCR and qPCR results indicated that recombinant pCas9-sgRNA plasmid can efficiently clear vanA-harboring plasmids. There was no significant correlation between sgRNA lengths and curing efficiency. In addition, the drug susceptibility test results showed that the bacterial resistance to vancomycin was significantly reduced after the vanA-containing drug-resistant plasmid was specifically cleaved by the CRISPR-Cas system. The CRISPR-Cas9 system can block the horizontal transfer of the conjugated plasmid pUC19-vanA. CONCLUSION: In conclusion, our study demonstrated that CRISPR-Cas9 achieved plasmid clearance and reduced antimicrobial resistance. The CRISPR-Cas9 system could block the horizontal transfer of plasmid carrying vanA. This strategy provided a great potential to counteract the ever-worsening spread of the vanA gene among bacterial pathogens and laid the foundation for subsequent research using the CRISPR-Cas9 system as adjuvant antibiotic therapy.

Elimination of blaKPC-2-mediated carbapenem resistance in Escherichia coli by CRISPR-Cas9 system.

OBJECTIVE: The purpose of this study is to re-sensitive bacteria to carbapenemases and reduce the transmission of the blaKPC-2 gene by curing the blaKPC-2-harboring plasmid of carbapenem-resistant using the CRISPR-Cas9 system. METHODS: The single guide RNA (sgRNA) specifically targeted to the blaKPC-2 gene was designed and cloned into plasmid pCas9. The recombinant plasmid pCas9-sgRNA(blaKPC-2) was transformed into Escherichia coli (E.coli) carrying pET24-blaKPC-2. The elimination efficiency in strains was evaluated by polymerase chain reaction (PCR) and quantitative real-time PCR (qPCR). Susceptibility testing was performed by broth microdilution assay and by E-test strips (bioMérieux, France) to detect changes in bacterial drug resistance phenotype after drug resistance plasmid clearance. RESULTS: In the present study, we constructed a specific prokaryotic CRISPR-Cas9 system plasmid targeting cleavage of the blaKPC-2 gene. PCR and qPCR results indicated that prokaryotic CRISPR-Cas9 plasmid transforming drug-resistant bacteria can efficiently clear blaKPC-2-harboring plasmids. In addition, the drug susceptibility test results showed that the bacterial resistance to imipenem was significantly reduced and allowed the resistant model bacteria to restore susceptibility to antibiotics after the blaKPC-2-containing drug-resistant plasmid was specifically cleaved by the CRISPR-Cas system. CONCLUSION: In conclusion, our study demonstrated that the one plasmid-mediated CRISPR-Cas9 system can be used as a novel tool to remove resistance plasmids and re-sensitize the recipient bacteria to antibiotics. This strategy provided a great potential to counteract the ever-worsening spread of the blaKPC-2 gene among bacterial pathogens and laid the foundation for subsequent research using the CRISPR-Cas9 system as adjuvant antibiotic therapy.

Peritonitis-associated with peritoneal dialysis following Ureaplasma parvum infection: A case report and literature review.

We report a patient diagnosed with peritonitis due to a rare infection of Ureaplasma parvum after receiving peritoneal dialysis for two years. This microorganism rarely causes peritoneal dialysis-associated peritonitis (PDAP). This is the first case of PDAP caused by Ureaplasma parvum. In the present case, the pathogen was identified through next-generation sequencing of PD fluid samples. The patient was treated with intraperitoneal (IP) levofloxacin combined with vancomycin and oral clarithromycin which effectively improved her symptoms. This case creates awareness that Ureaplasma parvum can cause PDAP and can be diagnosed using next-generation sequencing(NGS).

ESBL-Producing and Non-ESBL-Producing Escherichia coli Isolates from Urinary Tract Differ in Clonal Distribution, Virulence Gene Content and Phylogenetic Group.

Purpose: The objectives of this study are to determine the differences in clonality, virulence gene (VG) content and phylogenetic group between non extended-spectrum beta-lactamase-producing E. coli (non-ESBL-EC) and ESBL-EC isolates from urine. Patients and Methods: This study characterized a total of 100 clinical E. coli isolates consecutively obtained from the inpatients hospitalized in The First Affiliated Hospital of Ningbo University in China by polymerase-chain reaction (PCR). Results: Phylogenetic group B2 was found to be the most prevalent in both ESBL-EC and non-ESBL-EC group. Among 100 clinical isolates, the count of acquired virulence genes in group B2 was found to be significantly higher than that in group A, B1, and D (p <0.001). Additionally, the presence of content within virulence genes (the total number of virulence genes detected per isolate) in B2 of non-ESBL-EC and ESBL-EC showed a significant difference (p<0.001). ST131 was detected exclusively in ESBL-EC, while ST95 and ST73 were the main sequence types in non-ESBL-EC. Conclusion: Our study demonstrated the different distribution of MLST, phylogenetic group in ESBL-EC and non-ESBL-EC group. The inverse association between beta-lactamase resistance and VG content performed in this study should get a lot more attention. At the same time, we should also be wary of the appearance of non-ESBL-EC isolates of group B2 harboring more virulence genes which will lead to high pathogenicity.

Nanosecond Pulsed Electric Field Induces an Antitumor Effect in Triple-Negative Breast Cancer via CXCL9 Axis Dependence in Mice.

Triple-negative breast cancer (TNBC) is a refractory tumor, and therapeutic options are very limited. Local ablation has been applied recently. Chemokines play a critical role in the recruitment of immune cells into ablative tumors. Nanosecond pulsed electric field (nsPEF) shows potential anti-tumor efficacy, but the mechanism for maintaining the immune effect is not very clear. Here, we applied nsPEF for treating 4T1 breast cancer cells in vitro. RNA sequencing (RNA-seq) was applied. Anti-CXCL9 was used alone or combined with nsPEF to treat triple-negative breast cancer in mice. We demonstrated that nsPEF effectively induced cell apoptosis and inhibited the growth and metastasis of triple-negative breast cancer. An immune effect, especially chemotaxis, was activated by nsPEF. The number of infiltrated CD8+ T cells was increased significantly. We found that the inhibition of residual breast cancer growth by nsPEF was dependent on the CXCL9 axis. In conclusion, our work demonstrated that nsPEF effectively ablated the tumor, aroused an immune response, and inhibited residual breast cancer growth via CXCL9 axis dependence in mice.

MRI texture-based machine learning models for the evaluation of renal function on different segmentations: a proof-of-concept study.

BACKGROUND: To develop and validate an MRI texture-based machine learning model for the noninvasive assessment of renal function. METHODS: A retrospective study of 174 diabetic patients (training cohort, n = 123; validation cohort, n = 51) who underwent renal MRI scans was included. They were assigned to normal function (n = 71), mild or moderate impairment (n = 69), and severe impairment groups (n = 34) according to renal function. Four methods of kidney segmentation on T2-weighted images (T2WI) were compared, including regions of interest covering all coronal slices (All-K), the largest coronal slices (LC-K), and subregions of the largest coronal slices (TLCO-K and PIZZA-K). The speeded-up robust features (SURF) and support vector machine (SVM) algorithms were used for texture feature extraction and model construction, respectively. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of models. RESULTS: The models based on LC-K and All-K achieved the nonsignificantly highest accuracy in the classification of renal function (all p values > 0.05). The optimal model yielded high performance in classifying the normal function, mild or moderate impairment, and severe impairment, with an area under the curve of 0.938 (95% confidence interval [CI] 0.935-0.940), 0.919 (95%CI 0.916-0.922), and 0.959 (95%CI 0.956-0.962) in the training cohorts, respectively, as well as 0.802 (95%CI 0.800-0.807), 0.852 (95%CI 0.846-0.857), and 0.863 (95%CI 0.857-0.887) in the validation cohorts, respectively. CONCLUSION: We developed and internally validated an MRI-based machine-learning model that can accurately evaluate renal function. Once externally validated, this model has the potential to facilitate the monitoring of patients with impaired renal function.

Detection rate of SARS-CoV-2 RNA in relation to isolation time and environmental surface type.

Severe acute respiratory syndrome coronavirus (SARS-CoV-2) causes environmental contamination via respiratory droplets and persists on contaminants and environmental surfaces for anywhere from a few hours to 6 days. Therefore, it is particularly important to understand the transmission and containment of SARS-CoV-2 on the surface of objects within isolated environments. In this study, 356 environmental surface samples were collected and 79 tested positive, with the highest contamination rate (56.96%) in the wood category (bedside tables, wood floors, and walls). This study revealed differences in the detection rates of environmental surfaces in hospitalized and discharged rooms of patients with confirmed COVID-19 in 2 isolated settings (A: p = 0.001; B: p = 0.505) and suggested that environmental contamination may be an important route of virus transmission, providing a reference to guide the enhancement of ventilation, the use of hotel isolation model, the advocacy of cotton masks, and the effective suppression of virus transmission.

Thy-Wise: An interpretable machine learning model for the evaluation of thyroid nodules.

Current risk stratification systems for thyroid nodules suffer from low specificity and high biopsy rates. Recently, machine learning (ML) is introduced to assist thyroid nodule diagnosis but lacks interpretability. Here, we developed and validated ML models on 3965 thyroid nodules, as compared to the American College of Radiology Thyroid Imaging, Reporting and Data System (ACR TI-RADS). Subsequently, a SHapley Additive exPlanation (SHAP) algorithm was leveraged to interpret the results of the best-performing ML model. Clinical characteristics including thyroid-function tests were collected from medical records. Five ACR TI-RADS ultrasonography (US) categories plus nodule size were assessed by experienced radiologists. Random forest (RF), support vector machine (SVM) and extreme gradient boosting (XGBoost) were used to build US-only and US-clinical ML models. The ML models and ACR TI-RADS were compared in terms of diagnostic performance and unnecessary biopsy rate. Among the ML models, the US-only RF model (hereafter, Thy-Wise) achieved the optimal performance. Compared to ACR TI-RADS, Thy-Wise showed higher accuracy (82.4% vs 74.8% for the internal validation; 82.1% vs 73.4% for external validation) and specificity (78.7% vs 68.3% for internal validation; 78.5% vs 66.9% for external validation) while maintaining sensitivity (91.7% vs 91.2% for internal validation; 91.9% vs 91.1% for external validation), as well as reduced unnecessary biopsies (15.3% vs 32.3% for internal validation; 15.7% vs 47.3% for external validation). The SHAP-based interpretation of Thy-Wise enables clinicians to better understand the reasoning behind the diagnosis, which may facilitate the clinical translation of this model.

Real-time automatic prediction of treatment response to transcatheter arterial chemoembolization in patients with hepatocellular carcinoma using deep learning based on digital subtraction angiography videos.

BACKGROUND: Transcatheter arterial chemoembolization (TACE) is the mainstay of therapy for intermediate-stage hepatocellular carcinoma (HCC); yet its efficacy varies between patients with the same tumor stage. Accurate prediction of TACE response remains a major concern to avoid overtreatment. Thus, we aimed to develop and validate an artificial intelligence system for real-time automatic prediction of TACE response in HCC patients based on digital subtraction angiography (DSA) videos via a deep learning approach. METHODS: This retrospective cohort study included a total of 605 patients with intermediate-stage HCC who received TACE as their initial therapy. A fully automated framework (i.e., DSA-Net) contained a U-net model for automatic tumor segmentation (Model 1) and a ResNet model for the prediction of treatment response to the first TACE (Model 2). The two models were trained in 360 patients, internally validated in 124 patients, and externally validated in 121 patients. Dice coefficient and receiver operating characteristic curves were used to evaluate the performance of Models 1 and 2, respectively. RESULTS: Model 1 yielded a Dice coefficient of 0.75 (95% confidence interval [CI]: 0.73-0.78) and 0.73 (95% CI: 0.71-0.75) for the internal validation and external validation cohorts, respectively. Integrating the DSA videos, segmentation results, and clinical variables (mainly demographics and liver function parameters), Model 2 predicted treatment response to first TACE with an accuracy of 78.2% (95%CI: 74.2-82.3), sensitivity of 77.6% (95%CI: 70.7-84.0), and specificity of 78.7% (95%CI: 72.9-84.1) for the internal validation cohort, and accuracy of 75.1% (95% CI: 73.1-81.7), sensitivity of 50.5% (95%CI: 40.0-61.5), and specificity of 83.5% (95%CI: 79.2-87.7) for the external validation cohort. Kaplan-Meier curves showed a significant difference in progression-free survival between the responders and non-responders divided by Model 2 (p = 0.002). CONCLUSIONS: Our multi-task deep learning framework provided a real-time effective approach for decoding DSA videos and can offer clinical-decision support for TACE treatment in intermediate-stage HCC patients in real-world settings.

KIF4A promotes tumor progression of bladder cancer via CXCL5 dependent myeloid-derived suppressor cells recruitment.

Although KIF4A has been found to play an important role in a variety of tumors and is closely associated with the activation of immunocytes, its role in bladder cancer (BC) remains unclear. Here, we report increased expression of KIF4A in both lymph node-positive and high grade BC tissues. High expression of KIF4A has been significantly correlated with fewer CD8+ tumor-infiltrating lymphocytes (TILs) and a much worse prognosis in patients with BC. With respect to promoting tumor growth, the expression of KIF4A in promoting tumor growth was more pronounced in immune-competent mice (C57BL/6) than in immunodeficient mice (BALB/C). In addition, the more increased accumulation of myeloid-derived suppressor cells (MDSCs) was observed in tumor-bearing mice with KIF4A overexpression than in the control group. Transwell chemotaxis assays revealed that KIF4A overexpression in T24 cells increased MDSC recruitment. Furthermore, according to ELISA results, CXCL5 was the most noticeably increased cytokine in the KIF4A-transduced BC cells. Additional studies in vitro and in vivo showed that the capability of KIF4A to promote BC cells to recruit MDSCs could be significantly inhibited by anti-CXCL5 antibody. Therefore, our results demonstrated that KIF4A-mediated BC production of CXCL5 led to an increase in MDSC recruitment, which contributed to tumor progression.

Radiomics in precision medicine for gastric cancer: opportunities and challenges.

OBJECTIVES: Radiomic features derived from routine medical images show great potential for personalized medicine in gastric cancer (GC). We aimed to evaluate the current status and quality of radiomic research as well as its potential for identifying biomarkers to predict therapy response and prognosis in patients with GC. METHODS: We performed a systematic search of the PubMed and Embase databases for articles published from inception through July 10, 2021. The phase classification criteria for image mining studies and the radiomics quality scoring (RQS) tool were applied to evaluate scientific and reporting quality. RESULTS: Twenty-five studies consisting of 10,432 patients were included. 96% of studies extracted radiomic features from CT images. Association between radiomic signature and therapy response was evaluated in seven (28%) studies; association with survival was evaluated in 17 (68%) studies; one (4%) study analyzed both. All results of the included studies showed significant associations. Based on the phase classification criteria for image mining studies, 18 (72%) studies were classified as phase II, with two, four, and one studies as discovery science, phase 0 and phase I, respectively. The median RQS score for the radiomic studies was 44.4% (range, 0 to 55.6%). There was extensive heterogeneity in the study population, tumor stage, treatment protocol, and radiomic workflow amongst the studies. CONCLUSIONS: Although radiomic research in GC is highly heterogeneous and of relatively low quality, it holds promise for predicting therapy response and prognosis. Efforts towards standardization and collaboration are needed to utilize radiomics for clinical application. KEY POINTS: • Radiomics application of gastric cancer is increasingly being reported, particularly in predicting therapy response and survival. • Although radiomics research in gastric cancer is highly heterogeneous and relatively low quality, it holds promise for predicting clinical outcomes. • Standardized imaging protocols and radiomic workflow are needed to facilitate radiomics into clinical use.

Radiogenomic association between the T2-FLAIR mismatch sign and IDH mutation status in adult patients with lower-grade gliomas: an updated systematic review and meta-analysis.

OBJECTIVES: To reveal a radiogenomic correlation between the presence of the T2-fluid-attenuated inversion recovery resection (T2-FLAIR) mismatch sign on MR images and isocitrate dehydrogenase (IDH) mutation status in adult patients with lower-grade gliomas (LGGs). METHODS: A web-based systemic search for eligible literature up to April 13, 2021, was conducted on PubMed, Embase, and the Cochrane Library databases by two independent reviewers. This study was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. We included studies evaluating the accuracy of the T2-FLAIR mismatch sign in diagnosing the IDH mutation in adult patients with LGGs. The T2-FLAIR mismatch sign was defined as a T2-hyperintense lesion that is hypointense on FLAIR except for a hyperintense rim. RESULTS: Fourteen studies (n = 1986) were finally identified. The mean age of patients in the included studies ranged from 38.5 to 56 years. The pooled area under the curve (AUC), sensitivity, and specificity were obtained for each molecular profile: IDHmut-Codel: 0.46 (95% confidence interval [CI]: 0.42-0.50), 1% (95%CI: 0-7%), and 69% (95%CI: 62-75%), respectively; IDHmut-Noncodel: 0.75 (95%CI: 0.71-0.79), 42% (95%CI: 34-50%), and 99% (95%CI: 96-100%), respectively; IDH-Mutation regardless of 1p/19q codeletion status: 0.77 (95%CI: 0.73-0.80), 29% (95%CI: 21-40%), and 99% (95%CI: 92-100%), respectively. CONCLUSIONS: The T2-FLAIR mismatch sign was an insensitive but highly specific marker for IDHmut-Noncodel and IDH-Mutation LGGs, whereas it was not a useful marker for IDHmut-Codel LGGs. The findings might identify the T2-FLAIR mismatch sign as a non-invasive imaging biomarker for the selection of patients with IDH-mutant LGGs. KEY POINTS: • The T2-FLAIR mismatch sign was not a sensitive sign for IDH mutation in LGGs. • The T2-FLAIR mismatch sign was related to IDHmut-Noncodel with a specificity of 99%. • The pooled specificity (69%) of the T2-FLAIR mismatch sign for IDHmut-Codel was low.

Research on correlations of miR-585 expression with progression and prognosis of triple-negative breast cancer.

Triple-negative breast cancer is a special type of breast cancer, characterized by younger onset age, shorter survival period, higher malignant degree, higher mortality, recurrence and metastasis. Triple-negative breast cancer is more harmful to women's life and health, compared with other types of breast cancer. This paper mainly studied the role of miR-585 in triple-negative breast cancer. Real-time quantitative PCR was used to detect the expression of miR-585 in triple-negative breast cancer cell lines and tissues. Kaplan-Meier curve and Cox proportional hazards model analysis were used to investigate the prognostic value of miR-585 in triple-negative breast cancer. CCK-8 and Transwell assays were used to detect cell proliferation, invasion and migration. miR-585 was significantly down-regulated in triple-negative breast cancer cells and tissues. The low expression of miR-585 has been shown to be significantly associated with poor prognosis in triple-negative breast cancer patients. Abnormally low expression of miR-585 can promote cell proliferation, migration and invasion. Overall, abnormally low expression of miR-585 is associated with prognosis and progression of triple-negative breast cancer. miR-585 may serve as a prognostic biomarker for patients with triple-negative breast cancer and it is expected to be a new method and strategy for the treatment of triple-negative breast cancer.

Current status and quality of radiomic studies for predicting immunotherapy response and outcome in patients with non-small cell lung cancer: a systematic review and meta-analysis.

PURPOSE: Prediction of immunotherapy response and outcome in patients with non-small cell lung cancer (NSCLC) is challenging due to intratumoral heterogeneity and lack of robust biomarkers. The aim of this study was to systematically evaluate the methodological quality of radiomic studies for predicting immunotherapy response or outcome in patients with NSCLC. METHODS: We systematically searched for eligible studies in the PubMed and Web of Science datasets up to April 1, 2021. The methodological quality of included studies was evaluated using the phase classification criteria for image mining studies and the radiomics quality scoring (RQS) tool. A meta-analysis of studies regarding the prediction of immunotherapy response and outcome in patients with NSCLC was performed. RESULTS: Fifteen studies were identified with sample sizes ranging from 30 to 228. Seven studies were classified as phase II, and the remaining as discovery science (n = 2), phase 0 (n = 4), phase I (n = 1), and phase III (n = 1). The mean RQS score of all studies was 29.6%, varying from 0 to 68.1%. The pooled diagnostic odds ratio for predicting immunotherapy response in NSCLC using radiomics was 14.99 (95% confidence interval [CI] 8.66-25.95). In addition, radiomics could divide patients into high- and low-risk group with significantly different overall survival (pooled hazard ratio [HR]: 1.96, 95%CI 1.61-2.40, p < 0.001) and progression-free survival (pooled HR: 2.39, 95%CI 1.69-3.38, p < 0.001). CONCLUSIONS: Radiomics has potential to noninvasively predict immunotherapy response and outcome in patients with NSCLC. However, it has not yet been implemented as a clinical decision-making tool. Further external validation and evaluation within clinical pathway can facilitate personalized treatment for patients with NSCLC.

Resolution of serologic problems due to cold agglutinin mediated autoimmune hemolytic anemia and its transfusion decision.

BACKGROUND: Autoimmune hemolytic anemia (AIHA) is a rare disease characterized by hemolysis caused by autoantibodies against erythrocyte surface antigen. These antibodies can be classified as warm, cold, or mixed types. METHODS: We report two cases of cold agglutinin disease (CAD), which were eventually diagnosed owing to blood group discrepancy. Resolution was achieved after washing the red blood cells (RBCs) with warm saline and absorbing the autoantibodies at 4°C with the washed RBCs. We also assessed the patient's condition and discussed the strategy of blood transfusion. RESULTS: The first case occurred after postoperative chemotherapy for rectal cancer, and the other manifested with anemia from the outset. Direct antiglobulin tests were positive and revealed autoantibodies against C3d only. Cold agglutinin titration was performed, and the titers of both were 1:1024. Eventually, the patient's condition stabilized without blood transfusion. CONCLUSION: The serological discrepancies observed in the blood transfusion department can successfully guide blood transfusion decisions in cases of CAD.

Comparison of Clinical Features and CT Temporal Changes Between Familial Clusters and Non-familial Patients With COVID-19 Pneumonia.

Purpose: This study aimed to compare the clinical characteristics, laboratory findings, and chest computed tomography (CT) findings of familial cluster (FC) and non-familial (NF) patients with coronavirus disease 2019 (COVID-19) pneumonia. Methods: This retrospective study included 178 symptomatic adult patients with laboratory-confirmed COVID-19. The 178 patients were divided into FC (n = 108) and NF (n = 70) groups. Patients with at least two confirmed COVID-19 cases in their household were classified into the FC group. The clinical and laboratory features between the two groups were compared and so were the chest CT findings on-admission and end-hospitalization. Results: Compared with the NF group, the FC group had a longer period of exposure (13.1 vs. 8.9 days, p < 0.001), viral shedding (21.5 vs. 15.9 days, p < 0.001), and hospital stay (39.2 vs. 22.2 days, p < 0.001). The FC group showed a higher number of involved lung lobes on admission (3.0 vs. 2.3, p = 0.017) and at end-hospitalization (3.6 vs. 1.7, p < 0.001) as well as higher sum severity CT scores at end-hospitalization (4.6 vs. 2.7, p = 0.005) than did the NF group. Conversely, the FC group had a lower lymphocyte count level (p < 0.001) and a significantly lower difference in the number of involved lung lobes (Δnumber) between admission and discharge (p < 0.001). Notably, more cases of severe or critical illness were observed in the FC group than in the NF group (p = 0.036). Conclusions: Patients in the FC group had a worse clinical course and outcome than those in the NF group; thus, close monitoring during treatment and follow-ups after discharge would be beneficial for patients with familial infections.