Clinical prediction system of complications among patients with COVID-19: A development and validation retrospective multicentre study during first wave of the pandemic.

Clinical evidence suggests that some patients diagnosed with coronavirus disease 2019 (COVID-19) experience a variety of complications associated with significant morbidity, especially in severe cases during the initial spread of the pandemic. To support early interventions, we propose a machine learning system that predicts the risk of developing multiple complications. We processed data collected from 3,352 patient encounters admitted to 18 facilities between April 1 and April 30, 2020, in Abu Dhabi (AD), United Arab Emirates. Using data collected during the first 24 h of admission, we trained machine learning models to predict the risk of developing any of three complications after 24 h of admission. The complications include Secondary Bacterial Infection (SBI), Acute Kidney Injury (AKI), and Acute Respiratory Distress Syndrome (ARDS). The hospitals were grouped based on geographical proximity to assess the proposed system's learning generalizability, AD Middle region and AD Western & Eastern regions, A and B, respectively. The overall system includes a data filtering criterion, hyperparameter tuning, and model selection. In test set A, consisting of 587 patient encounters (mean age: 45.5), the system achieved a good area under the receiver operating curve (AUROC) for the prediction of SBI (0.902 AUROC), AKI (0.906 AUROC), and ARDS (0.854 AUROC). Similarly, in test set B, consisting of 225 patient encounters (mean age: 42.7), the system performed well for the prediction of SBI (0.859 AUROC), AKI (0.891 AUROC), and ARDS (0.827 AUROC). The performance results and feature importance analysis highlight the system's generalizability and interpretability. The findings illustrate how machine learning models can achieve a strong performance even when using a limited set of routine input variables. Since our proposed system is data-driven, we believe it can be easily repurposed for different outcomes considering the changes in COVID-19 variants over time.

SOCS3 gene silencing does not occur through methylation and mutations in gastric cancer.

Gastric cancer (GC) is ranked the third leading cause of cancer-related deaths worldwide. Mutations and epigenetic alterations in several essential genes, including p53, KRAS, PIK3CA, FAT4 and ARID1A, are often reported. Furthermore, loss of SOCS3 expression was reported in GC, suggesting its tumor suppressor role. To assess the mutational and methylation status of SOCS3, we performed gene panel exome sequencing on 47 human GC samples. The SOCS3 gene was rarely mutated, suggesting alternative regulation mechanisms, such as promoter hypermethylation and/or long non-coding RNAs (lncRNAs). We first explored SOCS3 promoter methylation status in 44 human GC samples by methylation-specific PCR (MS-PCR). Thirteen out of forty-four patients (29.5%) displayed a methylation pattern. Then, to see whether SOCS3 expression is silenced by CpG methylation, we examined publicly available databases (cbioportal and The Cancer Genome Atlas (TCGA)). The analysis revealed ß values lower than 0.1, indicating hypo-methylation in healthy and GC samples. Moreover, moderate methylation (ß < 0.4) and high methylation (ß > 0.4) did not affect the free survival, suggesting that methylation is unlikely to be the mechanism ruling SOCS3 silencing in GC. Next, to assess the regulatory effects of lncRNAs on SOCS3, we silenced the AC125807.2-lncRNA and quantified the SOCS3 gene expression in AGS and NCI-N87 gastric cancer cell line. SOCS3 was found to be downregulated following AC125807.2-lncRNA silencing in AGS cells, suggesting the potential implication of lncRNA AC125807.2 in SOCS3 regulation. However, in NCI-N87 cells, there was no significant change in SOCS3 expression. In conclusion, neither mutations nor hypermethylation was associated with the SOCS3 downregulation in GC, and alternative mechanisms, including non-coding RNAs-mediated gene silencing, may be proposed.

Outcomes of patients with end stage kidney disease on dialysis with COVID-19 in Abu Dhabi, United Arab Emirates; from PCR to antibody.

BACKGROUND: Individuals with end-stage kidney disease (ESKD) on dialysis are vulnerable to contracting COVID-19 infection, with mortality as high as 31 % in this group. Population demographics in the UAE are dissimilar to many other countries and data on antibody responses to COVID-19 is also limited. The objective of this study was to describe the characteristics of patients who developed COVID-19, the impact of the screening strategy, and to assess the antibody response to a subset of dialysis patients. METHODS: We retrospectively examined the outcomes of COVID19 infection in all our haemodialysis patients, who were tested regularly for COVID 19, whether symptomatic or asymptomatic. In addition, IgG antibody serology was also performed to assess response to COVID-19 in a subset of patients. RESULTS: 152 (13 %) of 1180 dialysis patients developed COVID-19 during the study period from 1st of March to the 1st of July 2020. Of these 81 % were male, average age of 52​ years and 95 % were on in-centre haemodialysis. Family and community contact was most likely source of infection in most patients. Fever (49 %) and cough (48 %) were the most common presenting symptoms, when present. Comorbidities in infected individuals included hypertension (93 %), diabetes (49 %), ischaemic heart disease (30 %). The majority (68 %) developed mild disease, whilst 13 % required critical care. Combinations of drugs including hydroxychloroquine, favipiravir, lopinavir, ritonavir, camostat, tocilizumab and steroids were used based on local guidelines. The median time to viral clearance defined by two negative PCR tests was 15 days [IQR 6-25]. Overall mortality in our cohort was 9.2 %, but ICU mortality was 65 %. COVID-19 IgG antibody serology was performed in a subset (n = 87) but 26 % of PCR positive patients (n = 23) did not develop a significant antibody response. CONCLUSIONS: Our study reports a lower mortality in this patient group compared with many published series. Asymptomatic PCR positivity was present in 40 %. Rapid isolation of positive patients may have contributed to the relative lack of spread of COVID-19 within our dialysis units. The lack of antibody response in a few patients is concerning.

Radiotherapy pre-treatment dose validation: A second verification of monitor units (MU) with a commercial software.

Inversely planned intensity-modulated radiotherapy (IMRT) and stereotactic small field radiotherapy should be verified before treatment execution. A second verification is carried out for planned treatments in IMRT and 3D conformal radiotherapy (3D-CRT) using a monitor verification commercial dose calculation management software (DCMS). For the same reference point the ion-chamber measured doses are compared for IMRT plans. DCMS (Diamond) computes dose based on modified Clarkson integration, accounting for multi-leaf collimators (MLC) transmission and measured collimator scatter factors. DCMS was validated with treatment planning system (TPS) (Eclipse 6.5 Version, Varian, USA) separately. Treatment plans computed from TPS are exported to DCMS using DICOM interface. Doses are re-calculated at selected points for fields delivered to IMRT phantom (IBA Scanditronix Wellhofer) in high-energy linac (Clinac 2300 CD, Varian). Doses measured at central axis, for the same points using CC13 (0.13 cc) ion chamber with Dose 1 Electrometer (Scanditronix Wellhofer) are compared with calculated data on DCMS and TPS. The data of 53 IMRT patients with fields ranging from 5 to 9 are reported. The computed dose for selected monitor units (MU) by Diamond showed good agreement with planned doses by TPS. DCMS dose prediction matched well in 3D-CRT forward plans (0.8 ± 1.3%, n = 37) and in IMRT inverse plans (-0.1 ± 2.2%, n = 37). Ion chamber measurements agreed well with Eclipse planned doses (-2.1 ± 2.0%, n = 53) and re-calculated DCMS doses (-1.5 ± 2.6%, n = 37) in phantom. DCMS dose validation is in reasonable agreement with TPS. DCMS calculations corroborate well with ionometric measured doses in most of the treatment plans.