Ruxolitinib reduces severe CRS response by suspending CAR-T cell function instead of damaging CAR-T cells.

The therapeutic effect of CAR-T is often accompanied by sCRS, which is the main obstacle to the promotion of CAR-T therapy. The JAK1/2 inhibitor ruxolitinib has recently been confirmed as clinically effective in maintaining control over sCRS, however, its mechanism remains unclear. In this study, we firstly revealed that ruxolitinib significantly inhibited the proliferation of CAR-T cells without damaging viability, and induced an efficacy-favored differentiation phenotype. Second, ruxolitinib reduced the level of cytokine release not only from CAR-T cells, but also from other cells in the immune system. Third, the cytolytic activity of CAR-T cells was restored once the ruxolitinib was removed; however, the cytokines released from the CAR-T cells maintained an inhibited state to some degree. Finally, ruxolitinib significantly reduced the proliferation rate of CAR-T cells in vivo without affecting the therapeutic efficacy after withdrawal at the appropriate dose. We demonstrated pre-clinically that ruxolitinib interferes with both CAR-T cells and the other immune cells that play an important role in triggering sCRS reactions. This work provides useful and important scientific data for clinicians on the question of whether ruxolitinib has an effect on CAR-T cell function loss causing CAR-T treatment failure when applied in the treatment of sCRS, the answer to which is of great clinical significance.

Comparison of samples of blister fluid and scales in the diagnosis of dermatomycosis.

BACKGROUND: The successful diagnosis of dermatomycosis depends on specimen collection. Dermatomycosis is sampled mainly for scales, but there is a lack of research on specimens of blister fluid. OBJECTIVES: To explore whether blister fluid can diagnose dermatomycosis and compare blister fluid and scale specimens for dermatomycosis diagnosis. METHODS: From April to July 2021, we prospectively gathered 34 patients who needed to meet all inclusion criteria simultaneously and collected their blister fluid and scales as specimens. The two samples were tested by fluorescent stain microscopy, fungal culture and PCR, and the diagnosis results were compared. RESULTS: The blister fluid sample's sensitivity, specificity and accuracy were 90%, 100% and 94.1%, respectively, whereas the scales sample were 60%, 100% and 76.5%, respectively. The positive likelihood ratios were >10 for both blister fluid and scales specimen, and the negative likelihood ratios were not <0.1. On the Youden's index, the blister fluid specimen was 90%, and the scales specimen was 60%. As for the diagnostic odds ratio, both of them were >1. By fungal culture, we detected 14 cases of fungi in blister fluid and eight in scales. On PCR, 22 cases of fungi in blister fluid and ten in scales were identified. CONCLUSIONS: This study demonstrated that a sample of blister fluid had better sensitivity, accuracy and Youden's index in diagnosing dermatomycosis with blister fluid. Collection of blister fluid might compensate for the inadequacy of collecting only scales specimens for mycological testing.

Medication self-management among parents of children with epilepsy at a tertiary care center in Malaysia.

PURPOSE: Self-management is crucial in the management of chronic diseases. However, information is limited on medication self-management among parents of children with epilepsy. This study aimed to assess medication self-management among parents of children with epilepsy and its association with sociodemographic data, clinical characteristics, antiepileptic drug (AED) regimen complexity, and parent self-reported AED adherence. METHOD: A cross-sectional survey was conducted at a tertiary care center in Malaysia from February 2019 to June 2019. Parents of children with epilepsy who were on AED for at least 3 months and aged ≤18 years old were recruited. Medication self-management was assessed using a validated Pediatric Epilepsy Medication Self-Management Questionnaire (PEMSQ). A higher total score reflects better medication self-management. RESULTS: A total of 166 patients were recruited. The mean ±â€¯standard deviation (SD) age of patients was 8.20 ±â€¯5.21 years, and 51.8% and 36.7% of patients have generalized seizure and focal seizure, respectively. The mean ±â€¯SD PEMSQ score was 116.2 ±â€¯11.28 from a total score of 135. Among the four domains of PEMSQ, the barriers to treatment contributed to the lowest mean scores. Univariate analysis showed that the following were significantly associated with poorer medication self-management: differences in ethnicity, religion; higher number of medications; presence of comorbidities; inability to swallow tablets; and a more complex AED regimen. Other variables were not significant. Multivariate analysis showed that only ethnicity and presence of comorbidity remained independently significant (R2 = 0.14; F [4, 161] = 6.28; p < 0.001). Poorer medication self-management was observed in the Malaysian Chinese population than in the Malaysian Malay population (b = -8.52; p < 0.001) and in children with comorbidities than in those without comorbidities (b = -5.04, p = 0.01). CONCLUSION: The overall medication self-management was satisfactory. Barriers to treatment should be addressed to empower parents to achieve better medication self-management. Furthermore, medication self-management should be reinforced among Malaysian Chinese patients and children with comorbidities.

Magnetic resonance imaging-based deep learning model to predict multiple firings in double-stapled colorectal anastomosis.

BACKGROUND: Multiple linear stapler firings during double stapling technique (DST) after laparoscopic low anterior resection (LAR) are associated with an increased risk of anastomotic leakage (AL). However, it is difficult to predict preoperatively the need for multiple linear stapler cartridges during DST anastomosis. AIM: To develop a deep learning model to predict multiple firings during DST anastomosis based on pelvic magnetic resonance imaging (MRI). METHODS: We collected 9476 MR images from 328 mid-low rectal cancer patients undergoing LAR with DST anastomosis, which were randomly divided into a training set (n = 260) and testing set (n = 68). Binary logistic regression was adopted to create a clinical model using six factors. The sequence of fast spin-echo T2-weighted MRI of the entire pelvis was segmented and analyzed. Pure-image and clinical-image integrated deep learning models were constructed using the mask region-based convolutional neural network segmentation tool and three-dimensional convolutional networks. Sensitivity, specificity, accuracy, positive predictive value (PPV), and area under the receiver operating characteristic curve (AUC) was calculated for each model. RESULTS: The prevalence of ≥ 3 linear stapler cartridges was 17.7% (58/328). The prevalence of AL was statistically significantly higher in patients with ≥ 3 cartridges compared to those with ≤ 2 cartridges (25.0% vs 11.8%, P = 0.018). Preoperative carcinoembryonic antigen level > 5 ng/mL (OR = 2.11, 95%CI 1.08-4.12, P = 0.028) and tumor size ≥ 5 cm (OR = 3.57, 95%CI 1.61-7.89, P = 0.002) were recognized as independent risk factors for use of ≥ 3 linear stapler cartridges. Diagnostic performance was better with the integrated model (accuracy = 94.1%, PPV = 87.5%, and AUC = 0.88) compared with the clinical model (accuracy = 86.7%, PPV = 38.9%, and AUC = 0.72) and the image model (accuracy = 91.2%, PPV = 83.3%, and AUC = 0.81). CONCLUSION: MRI-based deep learning model can predict the use of ≥ 3 linear stapler cartridges during DST anastomosis in laparoscopic LAR surgery. This model might help determine the best anastomosis strategy by avoiding DST when there is a high probability of the need for ≥ 3 linear stapler cartridges.

[Construction and Identification of Leukemia Cell Line Stably Expressing CD123 and CLL-1].

OBJECTIVE: To construct an acute myeloid leukemia cell line stably expressing CD123-CLL1 so as to provide an "in vitro" model for studying the role of CD123 and CLL-1 in leukemia and the treatment targeting CD123 and CLL-1. METHODS: The recombinant plasmid of lentivirus was constructed by synthesizing CD123 and CLL-1 sequences and PCR homologous recombination. The lentivirus vector was packaged by three-plasmid packaging system. After collecting the supernatant of lentivirus, the virus titer was determined by quantitative PCR. K562 leukemia cells were collected and transtected with virus supernatant. Leukemia cell line stably expressing the target gene were screened by purinomycin. The expression levels of CD123 and CLL-1 were detected by RT-PCR and flow cytometry. RESULTS: The lentiviral vector was successfully constructed, and identified by agarose gel electrophoresis and gene sequencing, then the virus titer of the supernatant was up to 5.81×108 after quantitative PCR assay. The K562 leukemia cell line obtained positive expression cells after being infected by puromycin. The high expression of CD123 and CLL-1 was confirmed by RT-PCR, while the significantly high expression of CD123 and CLL-1 was confirmed by flow cytometry. CONCLUSION: Lentiviral vector expressing CD123-CLL1 has been successfully constructed, and K562 leukemia cell line stably expressing CD123 and CLL-1 has been successfully obtained.

Predicting Chemotherapeutic Response for Far-advanced Gastric Cancer by Radiomics with Deep Learning Semi-automatic Segmentation.

Purpose: To build a dual-energy computed tomography (DECT) delta radiomics model to predict chemotherapeutic response for far-advanced gastric cancer (GC) patients. A semi-automatic segmentation method based on deep learning was designed, and its performance was compared with that of manual segmentation. Methods: This retrospective study included 86 patients with far-advanced GC treated with chemotherapy from September 2016 to December 2017 (66 and 20 in the training and testing cohorts, respectively). Delta radiomics features between the baseline and first follow-up DECT were modeled by random forest to predict the chemotherapeutic response evaluated by the second follow-up DECT. Nine feature subsets from confounding factors and delta radiomics features were used to choose the best model with 10-fold cross-validation in the training cohort. A semi-automatic segmentation method based on deep learning was developed to predict the chemotherapeutic response and compared with manual segmentation in the testing cohort, which was further validated in an independent validation cohort of 30 patients. Results: The best model, constructed by confounding factors and texture features, reached an average AUC of 0.752 in the training cohort. Our proposed semi-automatic segmentation method was more time-effective than manual segmentation, with average saving-time of 11.2333 ± 6.3989 minutes and 9.9889 ±5.5086 minutes in the testing cohort and the independent validation cohort, respectively (both p < 0.05). The predictive ability of the semi-automatic segmentation was also better than that of the manual segmentation both in the testing cohort and the independent validation cohort (AUC: 0.728 vs. 0.687 and 0.828 vs. 0.749, respectively). Conclusion: DECT delta radiomics serves as a promising biomarker for predicting chemotherapeutic response for far-advanced GC. Semi-automatic segmentation based on deep learning shows the potential for clinical use with increased reproducibility and decreased labor costs compared to the manual version.

Paediatric in-patient prescribing errors in Malaysia: a cross-sectional multicentre study.

Background There is a lack of large comprehensive studies in developing countries on paediatric in-patient prescribing errors in different settings. Objectives To determine the characteristics of in-patient prescribing errors among paediatric patients. Setting General paediatric wards, neonatal intensive care units and paediatric intensive care units in government hospitals in Malaysia. Methods This is a cross-sectional multicentre study involving 17 participating hospitals. Drug charts were reviewed in each ward to identify the prescribing errors. All prescribing errors identified were further assessed for their potential clinical consequences, likely causes and contributing factors. Main outcome measures Incidence, types, potential clinical consequences, causes and contributing factors of the prescribing errors. Results The overall prescribing error rate was 9.2% out of 17,889 prescribed medications. There was no significant difference in the prescribing error rates between different types of hospitals or wards. The use of electronic prescribing had a higher prescribing error rate than manual prescribing (16.9 vs 8.2%, p < 0.05). Twenty eight (1.7%) prescribing errors were deemed to have serious potential clinical consequences and 2 (0.1%) were judged to be potentially fatal. Most of the errors were attributed to human factors, i.e. performance or knowledge deficit. The most common contributing factors were due to lack of supervision or of knowledge. Conclusions Although electronic prescribing may potentially improve safety, it may conversely cause prescribing errors due to suboptimal interfaces and cumbersome work processes. Junior doctors need specific training in paediatric prescribing and close supervision to reduce prescribing errors in paediatric in-patients.